
#!pip install seaborn --upgrade
#!pip install --upgrade pip
#!pip install seaborn==0.9.0
!pip install tabulate
!pip install plotly
#!pip install sklearn

Requirement already satisfied: tabulate in c:\users\kevin\anaconda3\lib\site-packages (0.8.7)
Requirement already satisfied: plotly in c:\users\kevin\anaconda3\lib\site-packages (4.14.3)
Requirement already satisfied: six in c:\users\kevin\anaconda3\lib\site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in c:\users\kevin\anaconda3\lib\site-packages (from plotly) (1.3.3)


import numpy as np
import pandas as pd

import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
sns.set()
plt.style.use('fivethirtyeight')

import zipfile
import os # Used to interact with the file system

import re
from copy import deepcopy
from tabulate import tabulate

import plotly.express as px

import sklearn
import sklearn.datasets
import sklearn.linear_model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from sklearn.linear_model import LogisticRegression
from sklearn import linear_model as lm
from sklearn.feature_extraction import DictVectorizer

from IPython.display import display, HTML

import warnings
warnings.filterwarnings('ignore')

# Note: If you're having problems with the 3d scatter plots, uncomment the two lines below, and you should see a version that 
#      number that is at least 4.1.1.
# import plotly
# plotly.__version__


FILE_NAME = "e9"


USERNAME = "electric_k9"


# Error #1: use the regex below for replace
'''

\[[^\s]*Title\s"[^"]*"]\n


\[FEN[^\]]*\]\n\[SetUp[^\]]*\]\n


'''



# Error #2:
'''
BlackElo[^\]]*\]\n\[Variant
'''

'\nBlackElo[^\\]]*\\]\n\\[Variant\n'


def parse_pgn():

    pgn_input = pd.read_table(FILE_NAME + '.pgn', names=['r'])



    ###### 18 entries per game

    ### Column Names
    names = [
        "Event", #0
        "Site",
        "Date",
        "White",
        "Black",
        "Result", #5
        "UTCDate",
        "UTCTime",
        "WhiteElo",
        "BlackElo",
        "WhiteRatingDiff", #10
        "BlackRatingDiff",
        "Variant",
        "TimeControl",
        "ECO",
        "Opening", #15
        "Termination"
    ]



    ### Entry 0: special regex
    df = (pgn_input.iloc[0::18].reset_index()["r"]).str.extract("\[[a-zA-Z]*\s\"[^\"\s]*\s([^\"\s]*)").rename(columns={
        0:"Event"
    })


    # check for error
    rated = (pgn_input.iloc[0::18].reset_index()["r"]).str.findall("\[[a-zA-Z]*\s\"([^\"\s]*)")



    ### Entries 1-16: common regex
    for i in np.arange(1, 17):
        if (i != 2):
            fixed = (pgn_input.iloc[i::18].reset_index()["r"]).str.extract("\[[a-zA-Z]*\s\"([^\"]*)")
            df[names[i]] = fixed   


    # check for error
    for i in np.arange(len(df["Variant"])):
        if (df["Variant"][i] != "Standard"):
            for j in np.arange(len(df)):
                print(df.loc[j])
            print(df["Variant"])
            print(df.loc[i])
            raise ValueError('All games must be Standard variant.')

    del df["Variant"]


    ### Entry 17: game notation regex

    ## Game moves

    df["WhiteMoves"] = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]\.\s([^\s]*)")
    df["BlackMoves"] = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall(
        "[0-9]+\.\s[^\s]*\s([a-zA-Z][^\s]*) | }\s([a-zA-Z][^\s]*) | }\s[0-9]*\.\.\.\s([a-zA-Z][^\s]*)")

    for i in np.arange(len(df["BlackMoves"])):
        index = 0
        for j in np.arange(len(df["BlackMoves"][i])):
            if (j == 0):
                if (df["BlackMoves"][i][j][1] != ''):
                    index = 1
                elif (df["BlackMoves"][i][j][2] != ''):
                    index = 2
            df["BlackMoves"][i][j] = df["BlackMoves"][i][j][index]

    moves = []
    for i in np.arange(len(df["WhiteMoves"])):
        moves.append(len(df["WhiteMoves"][i]))
        
    df["Moves"] = moves

    ## Engine rating

    whiteStockfish = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]+\.\s[^\s]*\s{\s\[%eval\s([^\]]*)")
    blackStockfish = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]+\.\.\.\s[^\s]*\s{\s\[%eval\s([^\]]*)")

    ratings = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("%eval\s([^\]]*)")

    # converting str to int

    for i in np.arange(len(whiteStockfish)):
        for j in np.arange(len(whiteStockfish[i])):
            if ('#-' in whiteStockfish[i][j]):
                whiteStockfish[i][j] = -15
            elif ('#' in whiteStockfish[i][j]) or (float(whiteStockfish[i][j]) > 15):
                whiteStockfish[i][j] = 15
            elif (float(whiteStockfish[i][j]) < -15):
                whiteStockfish[i][j] = -15
            else:
                whiteStockfish[i][j] = float(whiteStockfish[i][j])

    for i in np.arange(len(blackStockfish)):
        for j in np.arange(len(blackStockfish[i])):
            if ('#-' in blackStockfish[i][j]):
                blackStockfish[i][j] = -15
            elif ('#' in blackStockfish[i][j]) or (float(blackStockfish[i][j]) > 15):
                blackStockfish[i][j] = 15
            elif (float(blackStockfish[i][j]) < -15):
                blackStockfish[i][j] = -15
            else:
                blackStockfish[i][j] = float(blackStockfish[i][j])


    for i in np.arange(len(ratings)):
        for j in np.arange(len(ratings[i])):
            if ('#-' in ratings[i][j]):
                ratings[i][j] = -15
            elif ('#' in ratings[i][j]) or (float(ratings[i][j]) > 15):
                ratings[i][j] = 15
            elif (float(ratings[i][j]) < -15):
                ratings[i][j] = -15
            else:
                ratings[i][j] = float(ratings[i][j])


    # Deep copying rating from stockfish 
    whiteRating = []
    blackRating = []
    
    for i in np.arange(len(whiteStockfish)):
        whiteRating.append(deepcopy(whiteStockfish[i]))

    for i in np.arange(len(blackStockfish)):
        blackRating.append(deepcopy(blackStockfish[i]))


    # Calculating rating increase/decrease for every move

    for i in np.arange(len(ratings)):
        for j in np.arange(len(ratings[i])):
            if ((j != 0) & (j % 2 == 0)):
                whiteRating[i][int(j / 2)] = ratings[i][j] - ratings[i][j - 1]
            elif (j % 2 == 1):
                blackRating[i][int(j / 2)] = ratings[i][j] - ratings[i][j - 1]

    df["WhiteRating"] = whiteRating
    df["BlackRating"] = blackRating

    df["WhiteStockfish"] = whiteStockfish
    df["BlackStockfish"] = blackStockfish



    ## Move times (highest runtime)
    # opted for full coverage with hours inclusion as well, although >99% not needed

    # this part realllyy wasn't fun
    whiteHours = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]+\.\s[^%]*(?:%eval[^\[]*\[)*%clk\s([^:]*)")
    blackHours = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("(?:(?:})|(?:[0-9]+\.\.\.))\s[a-zA-Z][^%]*(?:%eval[^\[]*\[)*%clk\s([^:]*)")

    whiteMinutes = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]+\.\s[^%]*(?:%eval[^\[]*\[)*%clk\s[^:]*:([^:]*)")
    blackMinutes = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("(?:(?:})|(?:[0-9]+\.\.\.))\s[a-zA-Z][^%]*(?:%eval[^\[]*\[)*%clk\s[^:]*:([^:]*)")

    whiteSeconds = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("[0-9]+\.\s[^%]*(?:%eval[^\[]*\[)*%clk\s[^:]*:[^:]*:([^\]]*)")
    blackSeconds = (pgn_input.iloc[17::18].reset_index()["r"]).str.findall("(?:(?:})|(?:[0-9]+\.\.\.))\s[a-zA-Z][^%]*(?:%eval[^\[]*\[)*%clk\s[^:]*:[^:]*:([^\]]*)")

    assert len(whiteHours) == len(whiteMinutes) == len(whiteSeconds) == len(blackHours) == len(blackMinutes) == len(blackSeconds)


    # converting str to int

    for i in np.arange(len(whiteMinutes)):
        whiteHours[i] = list(map(int, whiteHours[i]))
        blackHours[i] = list(map(int, blackHours[i]))

        whiteMinutes[i] = list(map(int, whiteMinutes[i]))
        blackMinutes[i] = list(map(int, blackMinutes[i]))

        whiteSeconds[i] = list(map(int, whiteSeconds[i]))
        blackSeconds[i] = list(map(int, blackSeconds[i]))


    # convert to array to multiply minutes/hours with constants, since a list * c only lengthens the list size by c in python.
    # benchmark shows that mapping and list comprehensions are slower than conversion to array for almost all N

    whiteTotalSeconds = whiteHours.copy(deep=True)
    whiteTotalMinutes = whiteHours.copy(deep=True)

    blackTotalMinutes = blackHours.copy(deep=True)
    blackTotalSeconds = blackHours.copy(deep=True)

    for i in np.arange(len(whiteMinutes)):
        tmpW = np.array(whiteHours[i]) * 3600 + np.array(whiteMinutes[i]) * 60 + np.array(whiteSeconds[i])
        whiteTotalSeconds[i] = tmpW.tolist()
        whiteTotalMinutes[i] = (tmpW / 60).tolist()

        tmpB = np.array(blackHours[i]) * 3600 + np.array(blackMinutes[i]) * 60 + np.array(blackSeconds[i])
        blackTotalSeconds[i] = tmpB.tolist()
        blackTotalMinutes[i] = (tmpB / 60).tolist()

    df["WhiteTotalSeconds"] = whiteTotalSeconds
    df["BlackTotalSeconds"] = blackTotalSeconds

    df["WhiteTotalMinutes"] = whiteTotalMinutes
    df["BlackTotalMinutes"] = blackTotalMinutes
    

    ### Time Control

    # time (first part)
    time = df["TimeControl"].str.findall("([^\+]*)\+")
    for i in np.arange(len(time)):
        time[i] = (list(map(int, time[i])))[0]

    #increment (second part)
    increment = df["TimeControl"].str.findall("\+([^\+]*)")
    for i in np.arange(len(increment)):
        increment[i] = (list(map(int, increment[i])))[0]

    df["Time"] = time
    df["Increment"] = increment
    del df["TimeControl"]



    ## Move Times (part II)
    # Deep copying times from TotalSeconds/TotalMinutes 

    for i in np.arange(len(whiteTotalSeconds)):
        whiteSeconds[i] = deepcopy(whiteTotalSeconds[i])
        whiteMinutes[i] = deepcopy(whiteTotalMinutes[i])

    for i in np.arange(len(blackTotalSeconds)):
        blackSeconds[i] = deepcopy(blackTotalSeconds[i])
        blackMinutes[i] = deepcopy(blackTotalMinutes[i])


    # Calculating time spent for every move

    for i in np.arange(len(whiteTotalSeconds)):
        for j in np.arange(len(whiteTotalSeconds[i])):
            if (j == 0):
                whiteSeconds[i][j] = 0
                whiteMinutes[i][j] = 0
            else:
                whiteSeconds[i][j] = whiteTotalSeconds[i][j - 1] - whiteTotalSeconds[i][j] + increment[i]
                whiteMinutes[i][j] = whiteSeconds[i][j] / 60

    for i in np.arange(len(blackTotalSeconds)):
        for j in np.arange(len(blackTotalSeconds[i])):
            if (j == 0):
                blackSeconds[i][j] = 0
                blackMinutes[i][j] = 0
            else:
                blackSeconds[i][j] = blackTotalSeconds[i][j - 1] - blackTotalSeconds[i][j] + increment[i]
                blackMinutes[i][j] = blackSeconds[i][j] / 60

    df["BlackSeconds"] = blackSeconds
    df["WhiteSeconds"] = whiteSeconds

    df["BlackMinutes"] = blackMinutes
    df["WhiteMinutes"] = whiteMinutes




    ### Switching to player view

    ## Player Color

    df["Color"] = "White"
    df.loc[df["Black"] == USERNAME, "Color"] = "Black"


    ## Opponent

    df["Opponent"] = df["White"]
    df.loc[df["Black"] != USERNAME, "Opponent"] = df["Black"]
    del df["White"]
    del df["Black"]


    ## Result

    # regex and loop for first part of result input
    result = df["Result"].str.findall("([^-]*)-")

    for i in np.arange(len(result)):
        if (result[i][0] == '1/2'):
            result[i] = 0.5
        else:
            result[i] = (list(map(int, result[i])))[0]

    df["Result"] = result

    # flip result if player is black
    df.loc[df["Color"] == "Black", "Result"] = 1 - df["Result"]


    # add win, loss, draw
    df["Win"] = (df["Result"] == 1.0).astype(int)
    df["Loss"] = (df["Result"] == 0.0).astype(int)
    df["Draw"] = (df["Result"] == 0.5).astype(int)

    ## Elo

    # Self Elo
    df["Rating"] = df["WhiteElo"]
    df.loc[df["Color"] == "Black", "Rating"] = df["BlackElo"]

    # Opponent Elo
    df["OpponentRating"] = df["WhiteElo"]
    df.loc[df["Color"] == "White", "OpponentRating"] = df["BlackElo"]

    del df["WhiteElo"]
    del df["BlackElo"]


    ## Rating Diff

    # Self Elo
    df["RatingChange"] = df["WhiteRatingDiff"]
    df.loc[df["Color"] == "Black", "RatingChange"] = df["BlackRatingDiff"]

    del df["WhiteRatingDiff"]
    del df["BlackRatingDiff"]


    ## Date/Time

    # year
    year = df["UTCDate"].str.findall("([^\.]*)\.")

    for i in np.arange(len(year)):
        year[i] = (list(map(int, year[i])))[0]

    df["Year"] = year

    # month
    month = df["UTCDate"].str.findall("\.([^\.]*)\.")

    for i in np.arange(len(month)):
        month[i] = (list(map(int, month[i])))[0]

    df["Month"] = month
    
    # day - has its specific purpose in a time graph
    day = df["UTCDate"].str.findall("\.[^\.]*\.([^\"]*)")

    for i in np.arange(len(day)):
        day[i] = (list(map(int, day[i])))[0]

    df["Day"] = day

    ## Opening

    # ECO code split
    letter = df["ECO"].str.findall("([A-Z])")
    number = df["ECO"].str.findall("[A-Z]([0-9]*)")

    for i in np.arange(len(letter)):
        if (letter[i] != []):
            letter[i] = (list(map(str, letter[i])))[0]
            number[i] = (list(map(int, number[i])))[0]
        else:
            letter[i] = ""
            number[i] = 0

    df["ECO1"] = letter
    df["ECO2"] = number

    # Variation
    variation = df["Opening"].str.findall(":\s(.*)")
    for i in np.arange(len(variation)):
        if (variation[i] != []):
            variation[i] = variation[i][0]
        else:
            variation[i] = "Main Line"

    df["Variation"] = variation


    # Opening
    opening = df["Opening"]
    for i in np.arange(len(variation)):
        opening[i] = re.search("([^:]*)", df["Opening"][i]).group()

    df["Opening"] = opening

    # Reordering
    cols = ['Year', 'Month', 'Day', 'Color', 'Result', 'Moves', 'Opponent', 'Opening', 'Variation', 
            'ECO', 'ECO1', 'ECO2', 'Time', 'Increment', 'Event', 
            'Rating', 'OpponentRating', 'RatingChange', 'WhiteMoves', 'BlackMoves', 'WhiteRating', 'BlackRating', 
           'WhiteStockfish', 'BlackStockfish', 'WhiteSeconds', 'BlackSeconds', 'WhiteMinutes', 'BlackMinutes', 
           'WhiteTotalSeconds', 'BlackTotalSeconds', 'WhiteTotalMinutes', 'BlackTotalMinutes', 
            'Win', 'Loss', 'Draw', 'Site', 'UTCDate', 'UTCTime',
           'Termination']
    df = df.reindex(columns = cols)
    
    return df


dp = parse_pgn()

dp


dp.to_csv(FILE_NAME + '.csv', index=False)


# FIX THESE
FILENAME = "e9"
USERNAME = "electric_k9"

# --------------------------------------------------


df = pd.read_csv(FILENAME + ".csv", converters={'WhiteMoves': eval, 'BlackMoves': eval,
                                               'WhiteStockfish': eval, 'BlackStockfish': eval,
                                               'WhiteRating': eval, 'BlackRating': eval,
                                               "WhiteSeconds":eval, "BlackSeconds":eval, 
                                                "WhiteMinutes":eval, "BlackMinutes":eval,
                                                "WhiteTotalSeconds":eval, "BlackTotalSeconds":eval, 
                                                "WhiteTotalMinutes":eval, "BlackTotalMinutes":eval})

white = df[df["Color"] == "White"]
black = df[df["Color"] == "Black"]

bullet = df[df["Event"] == "Bullet"]
blitz = df[df["Event"] == "Blitz"]
rapid = df[df["Event"] == "Rapid"]
classical = df[df["Event"] == "Classical"]


class color:
    PURPLE = '\033[95m'
    CYAN = '\033[96m'
    DARKCYAN = '\033[36m'
    BLUE = '\033[94m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    RED = '\033[91m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    END = '\033[0m'
    

def filter_df(df, col, name):
    return df[df[col] == name]

df


def wl_helper(data, side):
    results = data["Result"].value_counts().to_dict()
    gp = len(data["Result"])
    w = results.get(1, 0)
    l = results.get(0, 0)
    d = results.get(0.5, 0)
    if (w + l + d == 0):
        ppg = 0
    else:
        ppg = (w + 0.5 * d) / (w + l + d)
    
    print(color.BLUE + side)
    print(color.RED + "GP:\t\t" + str(gp))
    print("Record:\t\t" + str(w) + "-" + str(l) + "-" + str(d))
    print("PPG:\t\t" + str(round(ppg, 3)))
    
def wl(data, event):
    
    print(color.GREEN + event)
    wl_helper(data, "Overall:")
    mean = np.mean(data["OpponentRating"])
    print("AVG ELO:\t" + str(round(mean, 3)))
    
    wl_helper(data[data["Color"] == "White"], "White:")
    wl_helper(data[data["Color"] == "Black"], "Black:")
    
    print(color.END)


wl(df, "Total")
for i in ["Bullet", "Blitz", "Rapid", "Classical"]:
    if len(df[df["Event"] == i] != 0):
        wl(df[df["Event"] == i], i)

Total
Overall:
GP:		51
Record:		31-16-4
PPG:		0.647
AVG ELO:	1638.314
White:
GP:		22
Record:		15-4-3
PPG:		0.75
Black:
GP:		29
Record:		16-12-1
PPG:		0.569

Rapid
Overall:
GP:		37
Record:		23-12-2
PPG:		0.649
AVG ELO:	1636.486
White:
GP:		16
Record:		11-4-1
PPG:		0.719
Black:
GP:		21
Record:		12-8-1
PPG:		0.595

Classical
Overall:
GP:		14
Record:		8-4-2
PPG:		0.643
AVG ELO:	1643.143
White:
GP:		6
Record:		4-0-2
PPG:		0.833
Black:
GP:		8
Record:		4-4-0
PPG:		0.5


sns.scatterplot(data=df, x=df["Rating"] - df["OpponentRating"], y="RatingChange", s=30)
sns.set(rc={'figure.figsize':(12, 8)})

#plt.xlim(-600, 600)
plt.ylim(-100, 100)
#plt.legend()
plt.xlabel("Rating Difference (self - opponent)")
plt.ylabel("Rating Change")
#plt.title("")
plt.show()


plt.figure(figsize=(13,8))
urot = df.copy(deep = True)
urot = urot.sort_values(by="UTCDate", ascending=True)
ax = sns.lineplot(data=urot, x="UTCDate", y="Rating", hue="Event", style="Event", ci=None, sort=False)
plt.draw()
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")

plt.xlabel("Date")
plt.ylabel("Rating")
plt.title("Rating Over Time by Event")
plt.tight_layout()
plt.show()


plt.figure(figsize=(13,8))
urot = df.copy(deep = True)
urot["fulldate"] = urot["Year"] * 10000 + urot["Month"] * 100 + urot["Day"]

ax = sns.lineplot(data=urot, x="fulldate", y="Rating", hue="Event", style="Event", ci=None, sort=True)
plt.draw()
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")

plt.xlabel("Date")
plt.ylabel("Rating")
plt.title("Rating Over Time by Event")
plt.tight_layout()
plt.show()


N = 50


def perf_ELO_reg(df):
    plt.figure(figsize=(13,8))
    ax = sns.regplot(x=df["OpponentRating"], y=(df["Win"] + 0.5 * df["Draw"]) / (df["Win"] + df["Loss"] + df["Draw"]), 
                     x_bins=10)
    plt.draw()
    ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")

    plt.xlabel("Opponent ELO")
    plt.ylabel("PPG")
    plt.title("Performance against Opponent Rating")
    plt.tight_layout()
    plt.show()


def perf_ELO(df):
    mini = min(df["OpponentRating"])
    mini = round(mini / 50) * 50
    maxi = max(df["OpponentRating"])
    rating, score, count = [], [], []
    while (mini <= maxi):
        temp = df[(df["OpponentRating"] >= mini) & (df["OpponentRating"] < mini + N)]
        
        ppg = (np.sum(temp["Win"]) + 0.5 * np.sum(temp["Draw"])) / (
            np.sum(temp["Win"]) + np.sum(temp["Loss"]) + np.sum(temp["Draw"]))
        
        rating.append(str(mini) + "-" + str(mini + N))
        score.append(ppg)
        count.append(len(temp))
        
        mini += N
        
    plt.figure(figsize=(13,8))
    ax = sns.scatterplot(x=rating, y=score, size=count)
    plt.draw()
    ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")

    plt.xlabel("Opponent ELO")
    plt.ylabel("PPG")
    plt.title("Performance against Opponent Rating")
    plt.tight_layout()
    plt.show()


perf_ELO_reg(rapid)


perf_ELO(df)


def opening_list(data):
    # groupby
    df = data[["Opening", "Win", "Loss", "Draw", "Moves", "Event",
              "Variation", "Rating", "OpponentRating", 
              "RatingChange"]].groupby("Opening").agg({"Win":np.sum, "Loss":np.sum,
                                                    "Draw":np.sum, "Moves":np.average,
                                                    "Event":lambda x: x.value_counts().index[0],
                                                    "Variation": lambda x: x.value_counts().index[0],
                                                    "Rating":np.average, "OpponentRating":np.average,
                                                    "RatingChange":np.average})
    
    # create count col
    df["GP"] = df["Win"] + df["Loss"] + df["Draw"]
    # sort by count
    df = df.sort_values(by = "GP", ascending = False)
    # total ELO gained
    df["Total Rating Gain"] = (df["GP"] * df["RatingChange"]).astype(int)
    # PPG (points per game)
    df["PPG"] = round((df["Win"] + 0.5 * df["Draw"]) / (df["Win"] + df["Loss"] + df["Draw"]), 3)
    # comparison to average PPG as black
    avg_ppg = round((np.sum(data["Win"]) + 0.5 * np.sum(data["Draw"])) / 
                    (np.sum(data["Win"]) + np.sum(data["Loss"]) + np.sum(data["Draw"])), 3)
    df["REL"] = df["PPG"] - avg_ppg 
    # round stuff
    df["Moves"] = round(df["Moves"], 1)
    df["Rating"] = round(df["Rating"]).astype(int)
    df["OpponentRating"] = round(df["OpponentRating"]).astype(int)
    df["Rating Difference"] = df["Rating"] - df["OpponentRating"]
    df["RatingChange"] = round(df["RatingChange"], 2)
    # reorder columns
    df = df[["GP", "Moves", "Win", "Loss", "Draw", "PPG", "REL", "Total Rating Gain", "Rating Difference",
            "RatingChange", "Variation", "Event"]]
    df = df.rename(columns={"Event":"Most played in", "Variation":"Most common variation"})
    
    return df


opening_list(white).head(20)


opening_list(black).head(20)


# "White" or "Black"
side = "Black"

# Check spelling and capitalization!
opening = "Caro-Kann Defense"


def opening_variation_list(data):
    # groupby
    df = data[["Win", "Loss", "Draw", "Moves", "Event",
              "Variation", "Rating", "OpponentRating", 
              "RatingChange"]].groupby("Variation").agg({"Win":np.sum, "Loss":np.sum,
                                                    "Draw":np.sum, "Moves":np.average,
                                                    "Event":lambda x: x.value_counts().index[0],
                                                    "Rating":np.average, "OpponentRating":np.average,
                                                    "RatingChange":np.average})
    # create count col
    df["GP"] = df["Win"] + df["Loss"] + df["Draw"]
    # sort by count
    df = df.sort_values(by = "GP", ascending = False)
    # total ELO gained
    df["Total Rating Gain"] = (df["GP"] * df["RatingChange"]).astype(int)
    # PPG (points per game)
    df["PPG"] = round((df["Win"] + 0.5 * df["Draw"]) / (df["Win"] + df["Loss"] + df["Draw"]), 3)
    # comparison to average PPG as black
    avg_ppg = round((np.sum(data["Win"]) + 0.5 * np.sum(data["Draw"])) / 
                    (np.sum(data["Win"]) + np.sum(data["Loss"]) + np.sum(data["Draw"])), 3)
    df["REL"] = df["PPG"] - avg_ppg 
    # round stuff
    df["Moves"] = round(df["Moves"], 1)
    df["Rating"] = round(df["Rating"]).astype(int)
    df["OpponentRating"] = round(df["OpponentRating"]).astype(int)
    df["Rating Difference"] = df["Rating"] - df["OpponentRating"]
    df["RatingChange"] = round(df["RatingChange"], 2)
    # reorder columns
    df = df[["GP", "Moves", "Win", "Loss", "Draw", "PPG", "REL", "Total Rating Gain", "Rating Difference",
            "RatingChange", "Event"]]
    df = df.rename(columns={"Event":"Most played in"})
    
    print()
    print(color.BOLD + "OPENING: " + opening + " AS " + side)
    return df


opening_variation_list(df[df["Color"] == side][df["Opening"] == opening])

OPENING: Caro-Kann Defense AS Black


# Not being used right now.
# Aggregation function for stockfish related stuff.
def middle(a):
    b = []
    a = (a.reset_index().iloc[:,1])
    for i in np.arange(len(a)):
        if (a[i] != -100):
            b.append(a[i])
    
    if (b == []):
        b = [-100]
    return np.median(b)

def opening_tree(df, side, lst):
    if ((side != "White") & (side != "Black")):
        raise ValueError("Color must be black or white")
    
    self = df[df["Color"] == side].reset_index()
    if (side == "White"):
        opp = df[df["Color"] == "Black"].reset_index()
    else:
        opp = df[df["Color"] == "White"].reset_index()
        
    white = 1
    argIndex = 0
    
    for i in np.arange(len(lst) + 1):
        arr = []
        droplist = []
        
        # Update self tree
        if (white == 1):
            for j in np.arange(len(self["WhiteMoves"])):
                if (len(self["WhiteMoves"][j]) <= argIndex):
                    droplist.append(j)
                elif ((i != len(lst)) and (self["WhiteMoves"][j][argIndex] != lst[i])):
                    droplist.append(j)
        else:
            for j in np.arange(len(self["BlackMoves"])):
                if (len(self["BlackMoves"][j]) <= argIndex):
                    droplist.append(j)
                elif ((i != len(lst)) and (self["BlackMoves"][j][argIndex] != lst[i])):
                    droplist.append(j)
            if (i != len(lst)):
                argIndex += 1
            
                    
        self = self.drop(df.index[droplist]).reset_index().drop(["level_0"], axis=1)
        white = 1 - white

    white = 1 - white
    if (white == 0):
        sadge = "BLACK"
        orient = "Negative"
    else:
        sadge = "WHITE"
        orient = "Positive"
    
    # Table modifications 
    if (white == 1):
        # Random set
        self["Current Rating"] = self["BlackRating"]
        stockfish = "WhiteStockfish"
        moves = "WhiteMoves"
        ratin = "WhiteRating"
        antistockfish = "BlackStockfish"
    else:
        # Random set
        self["Current Rating"] = self["WhiteRating"]
        stockfish = "BlackStockfish"
        moves = "BlackMoves"
        ratin = "BlackRating"
        antistockfish = "WhiteStockfish"
    
    for j in np.arange(len(self[moves])):
        self[moves][j] = self[moves][j][argIndex]
        if (len(self[stockfish][j]) != 0):
            self[stockfish][j] = self[stockfish][j][argIndex]
            self[ratin][j] = self[ratin][j][argIndex]
            # Current rating
            if (moves == "WhiteMoves"):
                self["Current Rating"][j] = self[antistockfish][j][argIndex - 1]
            else:
                self["Current Rating"][j] = self[antistockfish][j][argIndex]
        else:
            self[stockfish][j] = -100
            self[ratin][j] = -100
            # Current rating
            self["Current Rating"][j] = -100

    
    # Since evals are different for the same moves sometimes, I switched from aggregating stockfish
    # stats using max to using median. This requires all rating related series to have dtype
    # int64, as np.median was not working for the aggregation and I was stuck for a long time.
    self[stockfish] = self[stockfish].astype(int)
    

    self_df = self[["Win", "Loss", "Draw", "Result", "Moves", 
              "Rating", "OpponentRating", 
                moves, stockfish,
                 ratin, "Opening",
                "Current Rating"]].groupby(moves).agg({"Opening":len,        
                                                    "Win":np.sum, "Loss":np.sum,
                                                    "Draw":np.sum, "Result": np.average, 
                                                    #"Opponent":lambda x: tuple(x),
                                                    "Rating":np.average, "OpponentRating":np.average,
                                                    "Moves":np.average, 
                                                       "Current Rating":max,
                                                    ratin:max,  stockfish:max })
    # Some adjustments
    self_df["Rating"] = round(self_df["Rating"], 1)
    self_df["OpponentRating"] = round(self_df["OpponentRating"], 1)
    self_df["Moves"] = round(self_df["Moves"], 1)
    self_df["Result"] = round(self_df["Result"], 3)
    self_df = self_df.rename(columns={"Opening":"Count", ratin:"EVAL",
                                     stockfish:"Rating After Move"}).sort_values("Count", ascending=False)
    self_df = self_df.replace(-100, "N/A")
    
    
    #return self_df.to_html('temp.html')
    
    print("")
    print(color.YELLOW + color.UNDERLINE + color.BOLD + USERNAME + " PLAYING " + side + color.END)
    print("")
    print("")
    if (sadge.lower() == side.lower()):
        print(color.BLUE + color.UNDERLINE + color.BOLD + sadge + " TO MOVE (" + orient + " rating is good)" + color.END)
    else:
        print(color.RED + color.UNDERLINE + color.BOLD + sadge + " TO MOVE (" + orient + " rating is good)" + color.END)
    display(self_df)


side = "White"
moves = ["e4", "e5"]

opening_tree(df, side, moves)

electric_k9 PLAYING White


WHITE TO MOVE (Positive rating is good)


def filter_stockfish(df):
    droplist = []
    for i in np.arange(len(df["WhiteStockfish"])):
        if (df["WhiteStockfish"][i] == []):
            droplist.append(i)
    
    df = df.drop(df.index[droplist]).reset_index()
    return df

stock_df = filter_stockfish(df)


def stockfish_fn(df, title, child, title2="", variation=""):
    
    # Build "empty" scores and moves list of right sizes
    scores = []
    moves = []
    player = []
    for j in np.arange(len(df["WhiteStockfish"])):
        scores.append([])
        moves.append([])
        player.append(0)

    for j in np.arange(len(df["WhiteStockfish"])):
        flipper = 0
        for i in np.arange(len(df["WhiteStockfish"][j] + df["BlackStockfish"][j])):
            if (flipper == 0):
                scores[j].append(df["WhiteStockfish"][j][int(i / 2)])
                moves[j].append(str(int(i / 2) + 1) + "W")
            else:
                scores[j].append(df["BlackStockfish"][j][int(i / 2)])
                moves[j].append(str(int(i / 2) + 1) + "_B")
            flipper = 1 - flipper

        if (df["Color"][j] == "White"):
            player[j] = "White"
        else:
            player[j] = "Black"

    #Plotting  
    plt.figure(figsize=(13,8))

    for i in np.arange(len(df["WhiteStockfish"])):
        if (player[i] == "White"):
            graphColor = "blue"
        else:
            graphColor = "red"
        if (child == "opvar"):
            if (df["Variation"][i] == variation):
                graphColor = "cyan"
        sns.lineplot(x=moves[i], y=scores[i], color=graphColor, sort=False)
        

    plt.xticks(rotation=90)
    plt.draw()

    plt.ylim(-16, 16)
    plt.xlabel("Move number")
    plt.ylabel("Stockfish Eval")
    if (child == "opp"):
        plt.title("Rating by Move against " + title)
    elif (child == "opening" or child == "opvar"):
        plt.title("Rating by Move for opening " + title + " as " + title2)
    #plt.tight_layout()
    plt.show()


# ex. Opponent_name = "HikaruGM"

Opponent_name = "thatprogrammer"


def stockfish_opp(df, opp):
    df = df[df["Opponent"] == opp].reset_index().drop(["level_0"], axis=1)
    stockfish_fn(df, opp, "opp")

stockfish_opp(stock_df, Opponent_name)


# ex. Opening = "Sicilian Defense"

Opening = "Caro-Kann Defense"

# "White" or "Black"
User_plays_as = "Black"


def stockfish_opening(df, opening, User_plays_as):
    df = df[df["Color"] == User_plays_as]
    df = df[df["Opening"] == opening].reset_index().drop(["level_0"], axis=1)
    stockfish_fn(df, opening, "opening", User_plays_as)

stockfish_opening(stock_df, Opening, User_plays_as)


# ex. Opening = "Four Knights Game"
# ex. Variation = "Italian Variation"

Opening = "Vienna Game"
Variation = "Vienna Gambit"

# "White" or "Black"
User_plays_as = "White"


def stockfish_opening_variation(df, opening, variation, User_plays_as):
    df = df[df["Color"] == User_plays_as]
    df = df[df["Opening"] == opening].reset_index().drop(["level_0"], axis=1)
    stockfish_fn(df, opening + ": " + variation, "opvar", User_plays_as, variation)

stockfish_opening_variation(stock_df, Opening, Variation, User_plays_as)


def stockfish_opening_variation_iso(df, opening, variation, User_plays_as):
    df = df[df["Color"] == User_plays_as]
    df = df[df["Opening"] == opening]
    df = df[df["Variation"] == variation].reset_index().drop(["level_0"], axis=1)
    stockfish_fn(df, opening + ": " + variation, "opening", User_plays_as, variation)

stockfish_opening_variation_iso(stock_df, Opening, Variation, User_plays_as)


# "White" or "Black"
user_color = "Black"


def move_rating(df, side):
    df = df[df["Color"] == side].reset_index()
    if (side == "White"):
        colrat = "WhiteRating"
        colmove = "WhiteMoves"
        enemyrat = "BlackRating"
        enemymove = "BlackMoves"
        bottomLimit = -11
        upperLimit = 1
        maxRat = -10
    elif (side == "Black"):
        colrat = "BlackRating"
        colmove = "BlackMoves"
        enemyrat = "WhiteRating"
        enemymove = "WhiteMoves"
        bottomLimit = -1
        upperLimit = 11
        maxRat = 10
    else:
        raise ValueError("Invalid input.")
        
    turn = []
    rating = []
    piece = []
    capture=[]
    # for the table below the graphs
    K, Q, R, B, N, C, P, X, NX = [], [], [], [], [], [], [], [], []
    
    for j in np.arange(len(df[colrat])):
        for i in np.arange(len(df[colrat][j])):
            # Added x_jitter by random (0, 0.4)
            turn.append(i + np.random.rand() * 0.8)
            if (abs(df[colrat][j][i]) > 10):
                rating.append(maxRat)
            else:
                rating.append(df[colrat][j][i])
            
            # movedPiece represents the piece moved already with [0] 
            # taking the first character of the string. 
            # ex. 'B' from 'Bc4'
            movedPiece = df[colmove][j][i][0]
            capturePiece = df[colmove][j][i][1]
            
            
            if (movedPiece == 'K'):
                piece.append('King')
                K.append(rating[-1])
            elif (movedPiece == 'Q'):
                piece.append('Queen')
                Q.append(rating[-1])
            elif (movedPiece == 'R'):
                piece.append('Rook')
                R.append(rating[-1])
            elif (movedPiece == 'B'):
                piece.append('Bishop')
                B.append(rating[-1])
            elif (movedPiece == 'N'):
                piece.append('Knight')
                N.append(rating[-1])
            elif (movedPiece == 'O'):
                piece.append('Castle')
                C.append(rating[-1])
            else:
                piece.append('Pawn')
                P.append(rating[-1])
            
            if (capturePiece == 'x'):
                capture.append('Capture')
                X.append(rating[-1])
            else:
                capture.append('No capture')
                NX.append(rating[-1])
                
                
    # added this way after or probably would've written differently
    e_turn = []
    e_rating = []
    e_piece = []
    e_capture=[]
    eK, eQ, eR, eB, eN, eC, eP, eX, eNX = [], [], [], [], [], [], [], [], []
    
    for j in np.arange(len(df[enemyrat])):
        for i in np.arange(len(df[enemyrat][j])):
            # Added x_jitter by random (0, 0.4)
            e_turn.append(i)
            if (abs(df[enemyrat][j][i]) > 10):
                e_rating.append(maxRat * -1)
            else:
                e_rating.append(df[enemyrat][j][i])
            
            # movedPiece represents the piece moved already with [0] 
            # taking the first character of the string. 
            # ex. 'B' from 'Bc4'
            e_movedPiece = df[enemymove][j][i][0]
            e_capturePiece = df[enemymove][j][i][1]
            
            
            if (e_movedPiece == 'K'):
                e_piece.append('King')
                eK.append(e_rating[-1])
            elif (e_movedPiece == 'Q'):
                e_piece.append('Queen')
                eQ.append(e_rating[-1])
            elif (e_movedPiece == 'R'):
                e_piece.append('Rook')
                eR.append(e_rating[-1])
            elif (e_movedPiece == 'B'):
                e_piece.append('Bishop')
                eB.append(e_rating[-1])
            elif (e_movedPiece == 'N'):
                e_piece.append('Knight')
                eN.append(e_rating[-1])
            elif (e_movedPiece == 'O'):
                e_piece.append('Castle')
                eC.append(e_rating[-1])
            else:
                e_piece.append('Pawn')
                eP.append(e_rating[-1])
            
            if (e_capturePiece == 'x'):
                e_capture.append('Capture')
                eX.append(e_rating[-1])
            else:
                e_capture.append('No capture')
                eNX.append(e_rating[-1])
    
    ax = plt.subplot()
    sns.set(rc={'figure.figsize':(12, 8)})
    sns.scatterplot(x=turn, y=rating, hue=piece, s=60)
    plt.xlabel("Move #")
    plt.ylabel("EVAL")
    plt.show()
    
    ax = plt.subplot()
    sns.set(rc={'figure.figsize':(12, 8)})
    sns.scatterplot(x=turn, y=rating, hue=capture, s=60)
    plt.xlabel("Move #")
    plt.ylabel("EVAL")
    plt.show()
    
    # Print table
    print("USER IS: " + side)
    
    looper = [[K, eK, "King"], [Q, eQ, "Queen"], [R, eR, "Rook"], [B, eB, "Bishop"], 
              [N, eN, "Knight"], [P, eP, "Pawn"], [C, eC, "Castle"], [X, eX, "Capture"], [NX, eNX, "No capture"]]
    piece_df = {"Piece":[], "Avg Rating":[], "Avg Opponent Rating":[], "Total Gain":[], "Count":[], "Opponent Count":[]}
    piece_df = pd.DataFrame(piece_df)
    for i in looper:
        new_row = {"Piece":i[2], "Avg Rating":round(np.average(i[0]), 2),  
                  "Avg Opponent Rating":round(np.average(i[1]), 2), 
                  "Total Gain":round((round(np.average(i[0]), 2) + round(np.average(i[1]), 2)) * (len(i[0]) + len(i[1])) / 2, 2),
                  "Count":len(i[0]),
                  "Opponent Count":len(i[1])}
        piece_df = piece_df.append(new_row, ignore_index = True)

    
    print(piece_df.set_index("Piece").to_markdown())
    
    
    for i in np.arange(6):
        print()
    
    
    
    # Individual plots now
    idf = pd.DataFrame([turn, rating, piece]).transpose()
    idf[1] = idf[1].astype(float)
    idf[0] = idf[0].astype(int)
    colors = ['goldenrod', 'red', 'dimgray', 'green', 'coral', 'deepskyblue', 'fuchsia']
    colorsCount = 0
    
    for i in ['King', 'Queen', 'Rook', 'Bishop', 'Knight', 'Pawn', 'Castle']:
        temp_idf = idf[idf[2] == i]
        
        print(i + " Graph: " + str(round(np.average(temp_idf[1]), 2)) + ", n=" + str(len(temp_idf)))
        ax = plt.subplot()
        sns.set(rc={'figure.figsize':(12, 8)})
        sns.scatterplot(x=temp_idf[0], y=temp_idf[1], s=60, color=colors[colorsCount])
        plt.xlabel("Move #")
        plt.ylabel("EVAL")
        plt.ylim(bottomLimit, upperLimit)
        plt.show()
        print("")
        print("")
        
        colorsCount += 1
    

move_rating(stock_df, user_color)

USER IS: Black
| Piece      |   Avg Rating |   Avg Opponent Rating |   Total Gain |   Count |   Opponent Count |
|:-----------|-------------:|----------------------:|-------------:|--------:|-----------------:|
| King       |         0.12 |                 -0.66 |       -32.67 |      44 |               77 |
| Queen      |         0.61 |                 -1.09 |       -47.76 |     113 |               86 |
| Rook       |         0.8  |                 -0.64 |        17.84 |     119 |              104 |
| Bishop     |         0.65 |                 -0.66 |        -1.32 |     127 |              137 |
| Knight     |         0.56 |                 -0.51 |         7.85 |     149 |              165 |
| Pawn       |         0.37 |                 -0.68 |       -73.16 |     236 |              236 |
| Castle     |         0.4  |                 -0.91 |       -10.46 |      20 |               21 |
| Capture    |         0.4  |                 -0.36 |         7.6  |     207 |              173 |
| No capture |         0.58 |                 -0.77 |      -119.13 |     601 |              653 |


King Graph: 0.12, n=44


Queen Graph: 0.61, n=113


Rook Graph: 0.8, n=119


Bishop Graph: 0.65, n=127


Knight Graph: 0.56, n=149


Pawn Graph: 0.37, n=236


Castle Graph: 0.4, n=20


# Inputs here

# Any number works (up to abs(15))
EVAL_RATING = -1.2

# Type either "range" or "split"
range_or_split = "range"

# Range for eval rating. If EVAL_RATING = 1.4 and EVAL_RANGE = 0.2, then it finds 
# games between 1.2 and 1.6 rating (inclusive).
# MUST BE POSITIVE!
EVAL_RANGE = 0.5

# Split for eval rating. If EVAL_RATING = 1.4 and SPLIT_UP = True, then take 
# all evals greater than or equal to 1.4. If SPLIT_UP = False, then take those below or equal.
SPLIT_UP = True

# Specify move number
MOVE_NUMBER = 5

# "White" or "Black"
user_color = "White"





# Optional: output indices + a dataframe of the games in this eval range. 

# Set to True or False.
output = True

# Type "Win", "Loss", or "Draw"
# in output_result for only games that were won, lost, or drawn, or anything
# else for all games.
output_result = "Win"


def eval_move(df, eval_rating, range_or_split, eval_range, split_up, move_number, output, side, output_result):
    
    df = df[df["Color"] == side].reset_index().drop(["level_0"], axis=1)
    
    if (side == "White"):
        ratin = "WhiteStockfish"
    elif (side == "Black"):
        ratin = "BlackStockfish"
    else:
        raise ValueError("Invalid Input on user_color.")
    
    droplist = []
    if (range_or_split == "range"):
        for i in np.arange(len(df[ratin])):
            
            if (len(df[ratin][i]) < move_number):
                droplist.append(i)

            elif (abs(df[ratin][i][move_number - 1] - eval_rating) > eval_range):
                #print(df[ratin][i][move_number - 1])
                droplist.append(i)
    
    elif (range_or_split == "split"):
        for i in np.arange(len(df[ratin])):
            if (len(df[ratin][i]) < move_number):
                droplist.append(i)
            elif (split_up and (df[ratin][i][move_number - 1] < eval_rating)):
                droplist.append(i)
            elif ((split_up == False) and (df[ratin][i][move_number - 1] > eval_rating)):
                droplist.append(i)
    else:
        raise ValueError("Invalid Input on range_or_split.")
        
    df = df.drop(df.index[droplist]).reset_index().drop(["level_0"], axis=1)
    
    for i in np.arange(len(df[ratin])):
        df[ratin][i] = df[ratin][i][move_number - 1]
    
    print(color.GREEN + "Eval Range: " + str(eval_rating - eval_range) + " <-> " + str(eval_rating + eval_range))
    print("Move Number: " + str(move_number))
    wl_helper(df, side)
    mean = np.mean(df["OpponentRating"])
    print("AVG ELO:\t" + str(round(mean, 3)))
    
    if (output == True):
        print("------------------")
        print("")
    
        if (output_result == "Win"):
            df = df[df["Win"] == 1]
        elif (output_result == "Loss"):
            df = df[df["Loss"] == 1]
        elif (output_result == "Draw"):
            df = df[df["Draw"] == 1]
        else:
            output_result = "All"
    
        print(color.END + "Indices from original dataframe (read right column) for " + output_result + ":")
        print(df["index"])
        return df
            
    
eval_move(stock_df, EVAL_RATING, range_or_split, EVAL_RANGE, SPLIT_UP, MOVE_NUMBER, output, user_color, output_result)

Eval Range: -1.7 <-> -0.7
Move Number: 5
White
GP:		0
Record:		0-0-0
PPG:		0
AVG ELO:	nan
------------------

Indices from original dataframe (read right column) for Win:
Series([], Name: index, dtype: int64)


def filter_time(df):
    droplist = []
    for i in np.arange(len(df["WhiteMinutes"])):
        if (df["WhiteMinutes"][i] == []):
            droplist.append(i)
    
    df = df.drop(df.index[droplist]).reset_index()
    return df

time_df = filter_time(df)


def time_fn(df, mode, time, increment):
    if (mode != ""):
        df = df[df["Event"] == mode].reset_index().drop(["level_0"], axis=1) 
    if (time != ""):
        df = df[df["Time"] == time].reset_index().drop(["level_0"], axis=1)
    if (increment != ""):
        df = df[df["Increment"] == increment].reset_index().drop(["level_0"], axis=1)
    
    if (len(df) == 0):
        print("No games found.")
        return
    
    user = []
    move = []
    timeSpent = []
    timeLeft = []
    pieces = []
    
    # The reason this is needed instead of just using timeSpent is because of an edge
    # case where checkmate happens instead of a forfeit/time-out. The clock will update for
    # the very last move, but no eval is shown, so the lengths of the two arrays will mismatch.
    timeStock = []
    stockfish = []
    ratin = []
    
    gameCount = 0
    
    dualColor = ["White", "Black"]
    dualSeconds = ["WhiteSeconds", "BlackSeconds"]
    dualTotalMinutes = ["WhiteTotalMinutes", "BlackTotalMinutes"]
    dualStockfish = ["WhiteStockfish", "BlackStockfish"]
    dualRatin = ["WhiteRating", "BlackRating"]
    dualPieces =["WhiteMoves", "BlackMoves"]
             
    for k in np.arange(2):
        for i in np.arange(len(df[dualSeconds[k]])):
            lenstock = len(df[dualStockfish[k]][i])
            gameCount += 1
            
            # Check if user is White for every game
            if (df["Color"][i] == dualColor[k]):
                side = "Self"
            else:
                side = "Opponent"
                
            # Check if stockfish %eval is included for this game (i).
            if (df[dualStockfish[k]][i] == []):
                stockInclude = False
            else:
                stockInclude = True
                      
            for j in np.arange(len(df[dualSeconds[k]][i])):
                
                if (df[dualPieces[k]][i][j] != ""):
                    user.append(side)
                    move.append(j)
                    timeSpent.append(df[dualSeconds[k]][i][j])
                    timeLeft.append(df[dualTotalMinutes[k]][i][j])

                    try:
                        movedPiece = df[dualPieces[k]][i][j][0]
                    except IndexError:
                        print(i)
                        print(j)
                        print(df[dualPieces[k]][i])
                    if (movedPiece.islower()):
                        movedPiece = 'P'
                    pieces.append(movedPiece)
                
                if ((stockInclude == True) and (j < lenstock - 1)):
                    timeStock.append(df[dualSeconds[k]][i][j])
                    stockfish.append(df[dualStockfish[k]][i][j])
                    ratin.append(df[dualRatin[k]][i][j])
                



    idf = pd.DataFrame([move, timeSpent, timeLeft, user, stockfish, timeStock, ratin, pieces]).transpose()
    idf[0] = idf[0].astype(int)
    idf[1] = idf[1].astype(float)
    idf[2] = idf[2].astype(float)
    idf[4] = idf[4].astype(float)
    idf[5] = idf[5].astype(float)
    idf[6] = idf[6].astype(float)
    idf = idf.rename(columns={0:"move", 1:"timeSpent", 2:"timeLeft", 3:"user", 4:"stockfish",
                             5:"timeSpentStockfish", 6:"rating", 7:"pieces"})

    return idf, gameCount, stockfish
    


# EDIT HERE
mode = ""
time = ""
increment = ""


    
    
ta_df, gameCount, stock_time = time_fn(time_df, mode, time, increment)


print()
print(color.BOLD + "Game Count: " + str(gameCount / 2) + color.END)
print()

sns.lineplot(data=ta_df, x="move", y="timeSpent", hue="user", estimator=np.mean)
plt.xlabel("Move number")
plt.ylabel("Time Spent")
plt.xlim(0, 30)
plt.title("Move Number vs Time Spent")
plt.tight_layout()
plt.show()

Game Count: 51.0


print()
print(color.BOLD + "Game Count: " + str(gameCount / 2) + color.END)
print()


sns.lineplot(data=ta_df, x="move", y="timeLeft", hue="user", estimator=np.mean)
plt.xlabel("Move number")
plt.ylabel("Time Left")
plt.xlim(0, 30)
plt.title("Move Number vs Time Left")
plt.tight_layout()
plt.show()

Game Count: 51.0


if (len(stock_time) == 0):
        print("No games found with stockfish.")
else:
    sns.kdeplot(data=ta_df, x="stockfish", y="timeSpentStockfish", hue="user")
    plt.xlabel("Stockfish Rating")
    plt.ylabel("Time Spent")
    plt.title("Stockfish Rating vs Time Spent")
    plt.tight_layout()
    
    # Adjust the value if needed
    plt.ylim(top=50)
    
    plt.show()


if (len(stock_time) == 0):
    print("No games found with stockfish.")
else:
    sns.kdeplot(data=ta_df, x="rating", y="timeSpentStockfish", hue="user")
    plt.xlabel("Move rating")
    plt.ylabel("Time Spent")
    plt.title("Move Rating vs Time Spent")
    plt.tight_layout()
    
    # Adjust the value if needed
    plt.xlim(-10, 10)
    plt.ylim(top=50)
    
    plt.show()


uniquePieceList = np.unique(ta_df["pieces"])
for i in uniquePieceList:
    temp_idf = ta_df[ta_df["pieces"] == i]
    print("PIECE: " + i)
    sns.relplot(data=temp_idf, x="move", y="timeSpent", hue="user", kind="line")
    plt.xlabel("Piece Moved")
    plt.ylabel("Time Spent")
    plt.ylim(bottom=0)
    plt.title("Piece Moved vs Time Spent")
    plt.tight_layout()
    plt.show()

PIECE: B

PIECE: K

PIECE: N

PIECE: O

PIECE: P

PIECE: Q

PIECE: R


N = 40


startELO = 1550
endELO = 1700


def season_fn(df, start, end, season, gp):
    
    
    df = df.sort_values(["Year", "Month"], ascending = (False, False))
    
    bottom = max(gp - N, 0)
    score = np.sum(df[bottom: gp]["Win"]) + 0.5 * np.sum(df[bottom: gp]["Draw"])
    
    old_start = start
    old_end = end
    
    x, y, z = [], [], []
    
    lvl1, lvl2, lvl3 = 0.55, 0.61, 0.67
    # 22, 24.5, 27
    # 
    # 18, 15.5, 13

    if (gp >= N):
        promote = ""
        magnitude = ""
        if (score >= N * lvl1):
            promote = "PROMOTION "
            magnitude = "+50"
            start += 50
            end += 50
            if (score >= N * lvl2):
                magnitude = "+100"
                start += 50
                end += 50
                if (score >= N * lvl3):
                    magnitude = "+150"
                    start += 50
                    end += 50
        if (score <= N * (1 - lvl1)):
            promote = "DEMOTION "
            magnitude = "-50"
            start -= 50
            end -= 50
            if (score <= N * (1 - lvl2)):
                magnitude = "-100"
                start -= 50
                end -= 50
                if (score <= N * (1 - lvl3)):
                    magnitude = "-150"
                    start -= 50
                    end -= 50

        df50 = df[gp - N: gp]
    
        x, y, z = season_fn(df[0: gp - N], start, end, season + 1, gp - N)
    
    else:
        df50 = df
        promote = ""
        magnitude = ""
        
    x.append(season)
    y.append(np.mean(df["OpponentRating"]))
    z.append(score)
    
    wl_season(df50, "Season " + str(season), promote, magnitude, old_start, old_end, score)


    white = df50[df50["Color"] == "White"]
    black = df50[df50["Color"] == "Black"]


    print(color.BOLD + "WHITE:" + color.END)
    print(opening_list(white).iloc[0:5, [0, 2, 3, 4, 1, 8, 7]].to_markdown())

    print(color.BOLD + "BLACK:" + color.END)
    print(opening_list(black).iloc[0:5, [0, 2, 3, 4, 1, 8, 7]].to_markdown())
    print("")
    print("")
    print("")
    print("")

    gp -= N
    
    return x, y, z
        
        
def wl_season(data, event, promote, magnitude, start, end, score):
    
    print(color.UNDERLINE + color.DARKCYAN + event + color.END)
    print(color.RED + "Division:\t" + color.GREEN + str(start) + "-" + str(end))
    
    wl_helper(data, color.RED + "PTS:\t\t" + color.GREEN + str(score))
    
    mean = np.mean(data["OpponentRating"])
    print("AVG ELO:\t" + str(round(mean, 3)))
    
    if (promote != ""):
        print(color.PURPLE + promote + magnitude)
        
    print(color.END)


    
season_output = season_fn(df, startELO, endELO, 1, len(df))

Season 2
Division:	1600-1750
PTS:		9.0
GP:		11
Record:		9-2-0
PPG:		0.818
AVG ELO:	1687.0

WHITE:
| Opening           |   GP |   Win |   Loss |   Draw |   Moves |   Rating Difference |   Total Rating Gain |
|:------------------|-----:|------:|-------:|-------:|--------:|--------------------:|--------------------:|
| Vienna Game       |    2 |     2 |      0 |      0 |    29.5 |                  58 |                  27 |
| Caro-Kann Defense |    1 |     1 |      0 |      0 |    13   |                 144 |                  11 |
| French Defense    |    1 |     1 |      0 |      0 |    25   |                  34 |                  14 |
| King's Pawn Game  |    1 |     1 |      0 |      0 |    23   |                  78 |                  26 |
BLACK:
| Opening             |   GP |   Win |   Loss |   Draw |   Moves |   Rating Difference |   Total Rating Gain |
|:--------------------|-----:|------:|-------:|-------:|--------:|--------------------:|--------------------:|
| Caro-Kann Defense   |    5 |     4 |      1 |      0 |    17.6 |                  56 |                  20 |
| East Indian Defense |    1 |     0 |      1 |      0 |    32   |                  87 |                 -19 |


Season 1
Division:	1550-1700
PTS:		24.0
GP:		40
Record:		22-14-4
PPG:		0.6
AVG ELO:	1624.925
PROMOTION +50

WHITE:
| Opening          |   GP |   Win |   Loss |   Draw |   Moves |   Rating Difference |   Total Rating Gain |
|:-----------------|-----:|------:|-------:|-------:|--------:|--------------------:|--------------------:|
| Vienna Game      |    7 |     4 |      1 |      2 |    21.6 |                  43 |                  85 |
| Sicilian Defense |    3 |     2 |      0 |      1 |    35   |                  52 |                 130 |
| French Defense   |    2 |     2 |      0 |      0 |    32   |                -144 |                 181 |
| Bishop's Opening |    1 |     0 |      1 |      0 |    12   |                   5 |                 -31 |
| Italian Game     |    1 |     1 |      0 |      0 |    41   |                -482 |                 204 |
BLACK:
| Opening               |   GP |   Win |   Loss |   Draw |   Moves |   Rating Difference |   Total Rating Gain |
|:----------------------|-----:|------:|-------:|-------:|--------:|--------------------:|--------------------:|
| Caro-Kann Defense     |   17 |     9 |      7 |      1 |    33.7 |                 -22 |                -312 |
| Scotch Game           |    2 |     1 |      1 |      0 |    24.5 |                 -96 |                 279 |
| Indian Game           |    1 |     1 |      0 |      0 |    43   |                  10 |                  21 |
| Italian Game          |    1 |     0 |      1 |      0 |     7   |                -413 |                 -28 |
| King's Indian Defense |    1 |     1 |      0 |      0 |    21   |                  50 |                  38 |


fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')

x = season_output[0]
y = season_output[1]
z = season_output[2]

ax.set_xlabel("Season")
ax.set_ylabel("AVG ELO")
ax.set_zlabel("Score")

ax.scatter(x, y, z)

plt.show()


fig = px.scatter_3d(x=season_output[0], y=season_output[1], z=season_output[2],
                    range_x = [-3, max(x) + 3], range_y = [min(y) - 100, max(y) + 100], range_z = [-5, 55],
                   labels={
                     "x": "Season",
                    "y":"AVG ELO",
                       "z":"Score"
                     },
                    title="Opponent Elo vs W/L by Season")
fig.show()


#plt.figure(figsize=(13,8))

ax = sns.scatterplot(x=season_output[1], y=season_output[2], hue=season_output[0])
plt.draw()

plt.xlabel("ELO")
plt.ylabel("Score")
plt.title("Opponent Elo vs W/L by Season")
plt.show()


def ohe(df, colname):
    vec = DictVectorizer()
    vec.fit(df[[colname]].to_dict(orient='records'))
    col_data = vec.transform(df[[colname]].to_dict(orient='records')).toarray()
    col_unique = vec.get_feature_names()
    col_ohe = pd.DataFrame(col_data, columns=col_unique)
    df = pd.concat([df, col_ohe], axis=1)
    #df = df.drop(columns=col_unique[0])
    return df

def unique_names(df, colname):
    vec = DictVectorizer()
    vec.fit(df[[colname]].to_dict(orient='records'))
    col_data = vec.transform(df[[colname]].to_dict(orient='records')).toarray()
    col_unique = vec.get_feature_names()
    return col_unique


def df_transformation(df, cols):
    data = df[cols]
    
    data = data.replace("Black", 0).replace("White", 1)
    data = ohe(data, "Event").drop("Event", axis=1)
    data = ohe(data, "ECO1").drop("ECO1", axis=1)
    data = ohe(data, "Termination").drop("Termination", axis=1)
    
    pca(data, cols)
    
    data["Result"] = df["Result"]
    return data


def pca(df, cols):
    
    # Center data
    df_mean = df.mean(axis=0)
    df_centered = df - df_mean
    
    
    # Scale - each col has unit variance
    df_centered_scaled = df_centered / np.sqrt(np.var(df_centered))

    # for the case where all values are 0, SVD doesn't converge
    df_centered_scaled = df_centered_scaled.fillna(0)
    
    # Break into components
    #df_centered_scaled.dropna(inplace=True)
    u, s, vt = np.linalg.svd(df_centered_scaled, full_matrices=False)

    v = vt.T
    P = df_centered @ v
    
    plt.figure(figsize=(12, 4))
    plt.bar(df.columns, vt[0, :], alpha=0.7)
    plt.xticks(df.columns, rotation=90)
    plt.show()
    
    
    # Scree plot
    plt.plot(s**2 / sum(s**2));
    #plt.xticks([1, 2, 3], [1, 2, 3]);
    plt.xlabel('PC #');
    plt.ylabel('Fraction of Variance Explained');
    plt.title('Fraction of Variance Explained by each Principal Component')
    
    return P

print("USER DATA FIRST, MODEL_DF SECOND ")
print()
log_df = df_transformation(df, ["Year", "Color", "Time", "Increment", "Event", "Rating", "OpponentRating", "ECO1", "ECO2", "Moves", "Termination"])


model_df_load = pd.read_csv("model_df.csv")
model_df = df_transformation(model_df_load, ["Year", "Color", "Time", "Increment", "Event", "Rating", "OpponentRating", "ECO1", "ECO2", "Moves", "Termination"])

USER DATA FIRST, MODEL_DF SECOND


def rmse(actual, predicted):
    return np.sqrt(np.sum((actual - predicted)**2) / len(actual))


def model(data, prnt, option, df="", events="", eco1="", self=False):
    
    data["Result_numeric"] = data["Result"]
    di = {0.0: "Loss", 0.5: "Draw", 1.0: "Win"}
    data = data.replace({"Result":di})
    
    test_size = 0.2
    train, test = train_test_split(data, test_size=test_size, 
                                  #random_state=40
                                 )

    # Logistic Regression
    X_train_log = process_data(train, option, events, eco1, self)
    y_train_log = train["Result"]
    
    model = LogisticRegression(multi_class='multinomial',solver ='newton-cg', max_iter = len(X_train_log))
    model.fit(X_train_log, y_train_log)
    
    
    training_score_log = model.score(X_train_log, y_train_log)
    
    X_test_log = process_data(test, option, events, eco1, self)
    y_test_log = test["Result"]
    
    test_score_log = model.score(X_test_log, y_test_log)
    
    
    #df["Result_numeric"] = df["Result"]
    #X_self_log = process_data(df, option, events)
    #y_self_log = df["Result"]
    #self_score_log = model.score(X_self_log, y_self_log)
    
    
    
    # Linear Regression
    X_train_lin = X_train_log
    y_train_lin = train["Result_numeric"]
    X_test_lin = X_test_log
    y_test_lin = test["Result_numeric"]
    
    model_ln = lm.LinearRegression(fit_intercept=True)
    model_ln.fit(X_train_lin, y_train_lin)
    y_predicted_train_lin = model_ln.predict(X_train_lin)
    y_predicted_test_lin = model_ln.predict(X_test_lin)
    
    
    training_rmse = rmse(y_predicted_train_lin, y_train_lin)
    
    test_rmse = rmse(y_predicted_test_lin, y_test_lin)
    
    if (prnt == True):
        print("Training set size: " + str(len(train)))
        print("Test set size: " + str(len(test)))
        print()
        print("Logistic")
        print("Training accuracy: " + str(training_score_log))
        print("Test accuracy: " + str(test_score_log))
        #if (df != ""):
            #print("Self accuracy: " + str(self_score_log))
        print()
        #print("Linear")
        #print("Training RMSE: " + str(training_rmse))
        #print("Test RMSE: " + str(test_rmse))
    
    #return model_ln.predict([[1,30,0,1387,1862,0,1,0,0,0]])
    #return y_predicted_train_lin
    return model, model_ln

def process_data(data, option, events, eco1, self):
    
    
    # Drops
    filter_eco = [col for col in data if col.startswith("ECO1")]
    if (option < 1):
        for i in filter_eco:
            data = data.drop(i, axis=1)
    elif (self == False):
        for i in filter_eco:
            if i not in eco1:
                data = data.drop(i, axis=1)
    data = data.drop("ECO2", axis=1)
            
        
    filter_ter = [col for col in data if col.startswith("Termination")]
    for i in filter_ter:
        data = data.drop(i, axis=1)
        
    
    # Events
    if (self == False):
        filter_eve = [col for col in data if col.startswith("Event=")]
        for i in filter_eve:
            if i not in events:
                data = data.drop(i, axis=1)
    
        
    data = data.drop(["Moves", "Year", "Result", "Result_numeric"], axis=1)
    
    if (self == False):
        data = data.drop(["Time", "Increment"], axis=1)
    else:
        data = data.drop(["Rating"], axis=1)

    return data


"""
color: enter "White" or "Black"
rating, opponentRating
event: enter "Blitz", "Bullet", "Classical", "Rapid", or anything else that showed up in "Event" for the PGN file.
"""

side = "Black"
rating = 1980
opponentrating = 1960
event = "Rapid"



standard = []
uni = unique_names(df, "Event")
allowed_uni = ["Event=Bullet", "Event=Blitz", "Event=Rapid", "Event=Classical"]
for i in uni:
    if i in allowed_uni:
        standard.append(i)
event_list = [int(("Event=" + event) == i) for i in standard]


model_1, model_2 = model(model_df, True, 0, df, standard)

if (side == "White"):
    side = 1
else:
    side = 0


concat_list = [side,  rating, opponentrating] + event_list


print(color.YELLOW + "PREDICTED RESULT: " + color.PURPLE + model_1.predict([concat_list])[0])
print(color.YELLOW + "PREDICTED PPG:    " + color.PURPLE + str(round(model_2.predict([concat_list])[0], 4)) + color.END)

Training set size: 39264
Test set size: 9816

Logistic
Training accuracy: 0.6336083944580277
Test accuracy: 0.6271393643031785

PREDICTED RESULT: Win
PREDICTED PPG:    0.5211


"""
color: enter "White" or "Black"
time, increment = time control, ex. 10+5 (set to 10 and 5, for 10 minutes and 5 second increment)
opponentRating
event: enter "Blitz", "Bullet", "Classical", "Rapid", or anything else that showed up in "Event" for the PGN file.
"""

side = "White"
time = 5
increment = 3
opponentrating = 1300

event = "Blitz"



model_1, model_2 = model(log_df, True, 0, self=True)

if (side == "White"):
    side = 1
else:
    side = 0

time = time * 60
    
uni = unique_names(df, "Event")
event_list = [int(("Event=" + event) == i) for i in uni]

concat_list = [side, time, increment, opponentrating] + event_list


print(color.YELLOW + "PREDICTED RESULT: " + color.PURPLE + model_1.predict([concat_list])[0])
print(color.YELLOW + "PREDICTED PPG:    " + color.PURPLE + str(round(model_2.predict([concat_list])[0], 4)) + color.END)

Training set size: 40
Test set size: 11

Logistic
Training accuracy: 0.675
Test accuracy: 0.36363636363636365

PREDICTED RESULT: Win
PREDICTED PPG:    0.6347


"""
color: enter "White" or "Black"
time, increment = time control, ex. 10+5 (set to 10 and 5, for 10 minutes and 5 second increment)
opponentRating
eco1 = letter of ECO opening (ex. "C" from C45)
event: enter "Blitz", "Bullet", "Classical", "Rapid", or anything else that showed up in "Event" for the PGN file.
"""

side = "White"
time = 1
increment = 0
opponentrating = 1100
eco1 = "C"
event = "Bullet"



model_1, model_2 = model(log_df, True, 1, self=True)

if (side == "White"):
    side = 1
else:
    side = 0

time = time * 60
    
uni = unique_names(df, "Event")
event_list = [int(("Event=" + event) == i) for i in uni]

eco = unique_names(df, "ECO1")
eco_list = [int(("ECO1=" + eco1) == i) for i in eco]

concat_list = [side, time, increment, opponentrating] + event_list + eco_list


print(color.YELLOW + "PREDICTED RESULT: " + color.PURPLE + model_1.predict([concat_list])[0])
print(color.YELLOW + "PREDICTED PPG:    " + color.PURPLE + str(round(model_2.predict([concat_list])[0], 4)) + color.END)

Training set size: 40
Test set size: 11

Logistic
Training accuracy: 0.625
Test accuracy: 0.6363636363636364

PREDICTED RESULT: Draw
PREDICTED PPG:    0.8102

	Year	Month	Day	Color	Result	Moves	Opponent	Opening	Variation	ECO	...	BlackTotalSeconds	WhiteTotalMinutes	BlackTotalMinutes	Win	Loss	Draw	Site	UTCDate	UTCTime	Termination
0	2021	3	14	Black	1	16	carloslayun	Caro-Kann Defense	Main Line	B10	...	[600, 604, 610, 606, 604, 575, 549, 521, 495, ...	[10.0, 10.033333333333333, 9.45, 9.45, 9.51666...	[10.0, 10.066666666666666, 10.166666666666666,...	1	0	0	https://lichess.org/vMsQMTFK	2021.03.14	23:35:53	Normal
1	2021	3	8	White	1	23	piffolopuf	King's Pawn Game	Nimzowitsch Defense	B00	...	[1500, 1491, 1467, 1456, 1445, 1440, 1432, 142...	[25.0, 24.85, 24.783333333333335, 24.733333333...	[25.0, 24.85, 24.45, 24.266666666666666, 24.08...	1	0	0	https://lichess.org/YRZYOj4x	2021.03.08	06:40:18	Time forfeit
2	2021	3	6	White	1	40	grazia121	Vienna Game	Main Line	C25	...	[720, 718, 716, 712, 710, 708, 705, 702, 700, ...	[12.0, 11.933333333333334, 11.866666666666667,...	[12.0, 11.966666666666667, 11.933333333333334,...	1	0	0	https://lichess.org/KvaXPfTb	2021.03.06	21:59:24	Normal
3	2021	3	4	Black	1	18	Puckster2	Caro-Kann Defense	Main Line	B10	...	[900, 898, 897, 884, 876, 871, 851, 792, 759, ...	[15.0, 14.966666666666667, 14.9, 14.8166666666...	[15.0, 14.966666666666667, 14.95, 14.733333333...	1	0	0	https://lichess.org/AwokYQfB	2021.03.04	19:55:23	Normal
4	2021	3	3	White	1	25	katoks	French Defense	Advance Variation, Paulsen Attack	C02	...	[1200, 1195, 1192, 1189, 1187, 1183, 1181, 117...	[20.0, 19.85, 19.683333333333334, 19.616666666...	[20.0, 19.916666666666668, 19.866666666666667,...	1	0	0	https://lichess.org/KTYmmSVk	2021.03.03	06:39:13	Normal
5	2021	3	3	Black	0	32	avatar71	East Indian Defense	Main Line	A48	...	[1200, 1195, 1179, 1171, 1165, 1121, 1102, 106...	[20.0, 19.916666666666668, 19.866666666666667,...	[20.0, 19.916666666666668, 19.65, 19.516666666...	0	1	0	https://lichess.org/RCSXTepB	2021.03.03	06:11:35	Normal
6	2021	3	1	Black	0	20	asuvor	Caro-Kann Defense	Two Knights Attack	B10	...	[900, 912, 879, 879, 816, 807, 786, 799, 756, ...	[15.0, 15.2, 15.033333333333333, 14.9666666666...	[15.0, 15.2, 14.65, 14.65, 13.6, 13.45, 13.1, ...	0	1	0	https://lichess.org/CsFaZmYS	2021.03.01	08:55:53	Normal
7	2021	3	1	White	1	19	MiguelRojoImpar23	Vienna Game	Main Line	C25	...	[600, 606, 608, 611, 609, 613, 607, 609, 613, ...	[10.0, 10.1, 10.033333333333333, 9.8, 9.783333...	[10.0, 10.1, 10.133333333333333, 10.1833333333...	1	0	0	https://lichess.org/Atqqpc7H	2021.03.01	08:11:41	Normal
8	2021	2	26	Black	1	17	Firsin	Caro-Kann Defense	Classical Variation	B18	...	[900, 898, 893, 877, 839, 806, 780, 774, 718, ...	[15.0, 14.966666666666667, 14.866666666666667,...	[15.0, 14.966666666666667, 14.883333333333333,...	1	0	0	https://lichess.org/9XagE6Z8	2021.02.26	08:25:23	Time forfeit
9	2021	2	25	White	1	13	fazan_admin	Caro-Kann Defense	Maróczy Variation	B12	...	[840, 839, 836, 819, 818, 815, 812, 809, 805, ...	[14.0, 13.833333333333334, 13.733333333333333,...	[14.0, 13.983333333333333, 13.933333333333334,...	1	0	0	https://lichess.org/Zj8E3E31	2021.02.25	04:38:06	Normal
10	2021	2	25	Black	1	17	SergVish	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[1200, 1198, 1196, 1182, 1174, 1147, 1133, 112...	[20.0, 19.9, 19.85, 19.75, 19.633333333333333,...	[20.0, 19.966666666666665, 19.933333333333334,...	1	0	0	https://lichess.org/Sr0dqmy9	2021.02.25	04:24:35	Normal
11	2021	2	20	White	1	25	Zlatokosy	Sicilian Defense	Alapin Variation	B22	...	[1200, 1202, 1199, 1201, 1186, 1160, 1133, 113...	[20.0, 19.983333333333334, 20.0, 20.0666666666...	[20.0, 20.033333333333335, 19.983333333333334,...	1	0	0	https://lichess.org/rIhBEYZK	2021.02.20	22:46:33	Normal
12	2021	2	20	Black	1	42	JokerLiar	Caro-Kann Defense	Main Line	B10	...	[720, 724, 721, 693, 695, 675, 678, 677, 626, ...	[12.0, 12.083333333333334, 12.183333333333334,...	[12.0, 12.066666666666666, 12.016666666666667,...	1	0	0	https://lichess.org/YCQ5Xeqp	2021.02.20	22:14:22	Normal
13	2021	2	20	White	1	22	ecureuil_69	Vienna Game	Vienna Gambit	C26	...	[600, 598, 597, 592, 599, 605, 604, 607, 614, ...	[10.0, 10.083333333333334, 10.183333333333334,...	[10.0, 9.966666666666667, 9.95, 9.866666666666...	1	0	0	https://lichess.org/UEkS8vIW	2021.02.20	21:53:34	Normal
14	2021	2	19	Black	1	38	domenico61	Caro-Kann Defense	Hillbilly Attack	B10	...	[900, 913, 924, 932, 941, 912, 908, 914, 920, ...	[15.0, 15.216666666666667, 15.15, 15.366666666...	[15.0, 15.216666666666667, 15.4, 15.5333333333...	1	0	0	https://lichess.org/xuQIMbJK	2021.02.19	09:30:32	Normal
15	2021	2	19	Black	0	38	PawnBlusher	Caro-Kann Defense	Main Line	B10	...	[600, 609, 617, 622, 597, 604, 597, 587, 582, ...	[10.0, 10.0, 9.783333333333333, 9.85, 9.966666...	[10.0, 10.15, 10.283333333333333, 10.366666666...	0	1	0	https://lichess.org/aNEPnO2h	2021.02.19	08:59:24	Normal
16	2021	2	18	Black	1	14	AlejandroM	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[900, 898, 881, 874, 868, 861, 857, 842, 824, ...	[15.0, 14.933333333333334, 14.733333333333333,...	[15.0, 14.966666666666667, 14.683333333333334,...	1	0	0	https://lichess.org/3E3H9SYJ	2021.02.18	23:49:53	Normal
17	2021	2	16	White	1	19	mladendjuric55	Vienna Game	Vienna Gambit	C26	...	[900, 897, 889, 886, 868, 860, 845, 815, 789, ...	[15.0, 14.95, 14.883333333333333, 14.75, 14.6,...	[15.0, 14.95, 14.816666666666666, 14.766666666...	1	0	0	https://lichess.org/1uCoQ5xN	2021.02.16	07:50:15	Normal
18	2021	1	25	Black	1	43	arcnaka	Indian Game	Main Line	A45	...	[900, 895, 885, 870, 865, 848, 807, 775, 748, ...	[15.0, 14.6, 14.566666666666666, 14.5333333333...	[15.0, 14.916666666666666, 14.75, 14.5, 14.416...	1	0	0	https://lichess.org/41y1I6dI	2021.01.25	04:15:58	Normal
19	2021	1	24	White	0.5	34	jackbauerpwrhour	Vienna Game	Anderssen Defense	C25	...	[900, 901, 909, 918, 920, 933, 937, 936, 862, ...	[15.0, 15.116666666666667, 15.216666666666667,...	[15.0, 15.016666666666667, 15.15, 15.3, 15.333...	0	0	1	https://lichess.org/TBOfLX2P	2021.01.24	03:25:22	Normal
20	2021	1	17	White	1	29	Ceron68	French Defense	Classical Variation, Steinitz Variation	C11	...	[900, 903, 905, 909, 911, 911, 894, 883, 871, ...	[15.0, 15.016666666666667, 14.983333333333333,...	[15.0, 15.05, 15.083333333333334, 15.15, 15.18...	1	0	0	https://lichess.org/FHDuWQXK	2021.01.17	22:53:38	Normal
21	2021	1	17	White	0	47	Master_Don_Pundon	Scandinavian Defense	Mieses-Kotroc Variation	B01	...	[480, 480, 478, 475, 473, 471, 468, 455, 450, ...	[8.0, 7.783333333333333, 7.75, 7.6, 7.53333333...	[8.0, 8.0, 7.966666666666667, 7.91666666666666...	0	1	0	https://lichess.org/BdrJ9pMU	2021.01.17	22:36:11	Time forfeit
22	2021	1	16	White	1	25	buongiorgio1	Vienna Game	Anderssen Defense	C25	...	[2700, 2702, 2701, 2700, 2693, 2674, 2661, 258...	[45.0, 45.13333333333333, 45.13333333333333, 4...	[45.0, 45.03333333333333, 45.016666666666666, ...	1	0	0	https://lichess.org/lGmFihVm	2021.01.16	21:52:18	Normal
23	2021	1	15	Black	0.5	46	Yousefaboelmagd	Caro-Kann Defense	Maróczy Variation	B12	...	[600, 609, 597, 586, 569, 571, 509, 507, 486, ...	[10.0, 10.116666666666667, 10.25, 10.383333333...	[10.0, 10.15, 9.95, 9.766666666666667, 9.48333...	0	0	1	https://lichess.org/JmzNA3Cm	2021.01.15	21:01:43	Normal
24	2021	1	14	Black	1	37	RichardSchach	Caro-Kann Defense	Main Line	B10	...	[300, 306, 305, 309, 316, 317, 321, 328, 334, ...	[5.0, 5.116666666666666, 5.2, 5.26666666666666...	[5.0, 5.1, 5.083333333333333, 5.15, 5.26666666...	1	0	0	https://lichess.org/aeV5aB0T	2021.01.14	23:23:12	Normal
25	2021	1	14	White	0	4	Hamdyamin28	Vienna Game	Main Line	C25	...	[1200, 1187, 1183, 1169]	[20.0, 20.066666666666666, 19.583333333333332,...	[20.0, 19.783333333333335, 19.716666666666665,...	0	1	0	https://lichess.org/JZS5MfJY	2021.01.14	23:21:18	Normal
26	2021	1	14	Black	0	12	psxt12	Caro-Kann Defense	Main Line	B10	...	[1200, 1198, 1197, 1185, 1182, 1180, 1167, 108...	[20.0, 19.816666666666666, 19.783333333333335,...	[20.0, 19.966666666666665, 19.95, 19.75, 19.7,...	0	1	0	https://lichess.org/LhTPZBxH	2021.01.14	23:15:59	Normal
27	2021	1	14	White	0	12	whichful	Bishop's Opening	Vienna Hybrid	C24	...	[960, 964, 963, 957, 963, 964, 971, 971, 976, ...	[16.0, 16.116666666666667, 16.166666666666668,...	[16.0, 16.066666666666666, 16.05, 15.95, 16.05...	0	1	0	https://lichess.org/5n52ZaQb	2021.01.14	23:10:49	Normal
28	2021	1	14	Black	0	14	chemaqe	London System	Main Line	A48	...	[900, 899, 892, 889, 884, 826, 807, 786, 781, ...	[15.0, 14.933333333333334, 14.816666666666666,...	[15.0, 14.983333333333333, 14.866666666666667,...	0	1	0	https://lichess.org/YDjgdKaK	2021.01.14	23:02:57	Normal
29	2021	1	14	Black	1	21	Prof-jones	King's Indian Defense	Four Pawns Attack	E76	...	[1800, 1816, 1834, 1853, 1870, 1888, 1906, 191...	[30.0, 30.3, 30.6, 30.9, 31.166666666666668, 3...	[30.0, 30.266666666666666, 30.566666666666666,...	1	0	0	https://lichess.org/Ze8jkIj9	2021.01.14	01:42:52	Normal
30	2021	1	13	White	0.5	40	aconito	Sicilian Defense	Alapin Variation	B22	...	[780, 777, 769, 738, 729, 717, 712, 707, 703, ...	[13.0, 12.966666666666667, 12.9, 12.5, 11.8833...	[13.0, 12.95, 12.816666666666666, 12.3, 12.15,...	0	0	1	https://lichess.org/JwOD3Uvu	2021.01.13	22:04:56	Normal
31	2021	1	12	White	1	30	ITAKAR	Vienna Game	Vienna Gambit	C26	...	[900, 888, 883, 882, 848, 830, 824, 817, 809, ...	[15.0, 14.95, 14.933333333333334, 14.85, 14.8,...	[15.0, 14.8, 14.716666666666667, 14.7, 14.1333...	1	0	0	https://lichess.org/xgw8uOYJ	2021.01.12	21:51:15	Normal
32	2021	1	9	Black	1	27	Hamzzza-Nailkin	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[540, 553, 566, 579, 592, 604, 599, 607, 620, ...	[9.0, 9.183333333333334, 9.316666666666666, 9....	[9.0, 9.216666666666667, 9.433333333333334, 9....	1	0	0	https://lichess.org/7keBysFD	2021.01.09	23:46:46	Normal
33	2021	1	9	White	1	29	Hikal23	Rat Defense	Small Center Defense	C00	...	[2700, 2707, 2713, 2706, 2698, 2695, 2702, 269...	[45.0, 45.03333333333333, 44.86666666666667, 4...	[45.0, 45.11666666666667, 45.21666666666667, 4...	1	0	0	https://lichess.org/vhplZygT	2021.01.09	03:27:41	Normal
34	2021	1	8	Black	1	48	JORGEAPRA	Caro-Kann Defense	Exchange Variation	B13	...	[900, 912, 926, 936, 936, 939, 944, 929, 934, ...	[15.0, 15.15, 15.216666666666667, 15.333333333...	[15.0, 15.2, 15.433333333333334, 15.6, 15.6, 1...	1	0	0	https://lichess.org/bHqP3Sq2	2021.01.08	22:47:13	Normal
35	2021	1	4	White	0.5	17	anthony_abogado21PWD	Vienna Game	Vienna Gambit, Paulsen Attack	C29	...	[1800, 1706, 1672, 1616, 1564, 1519, 1475, 141...	[30.0, 30.1, 30.216666666666665, 30.2333333333...	[30.0, 28.433333333333334, 27.866666666666667,...	0	0	1	https://lichess.org/NEhQ6lVr	2021.01.04	02:07:56	Normal
36	2021	1	4	Black	0	56	Runa1791Runoid	Caro-Kann Defense	Exchange Variation	B13	...	[1800, 1811, 1820, 1822, 1832, 1817, 1826, 182...	[30.0, 29.866666666666667, 29.233333333333334,...	[30.0, 30.183333333333334, 30.333333333333332,...	0	1	0	https://lichess.org/oDSswIx0	2021.01.04	01:02:57	Normal
37	2021	1	3	Black	1	24	Numisma9999	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[1080, 1089, 1085, 1093, 1101, 1109, 1114, 111...	[18.0, 17.983333333333334, 17.983333333333334,...	[18.0, 18.15, 18.083333333333332, 18.216666666...	1	0	0	https://lichess.org/PDHTPoBA	2021.01.03	07:26:17	Normal
38	2021	1	3	White	1	40	InBetweenWins	Sicilian Defense	Alapin Variation	B22	...	[1800, 1808, 1814, 1823, 1811, 1812, 1800, 179...	[30.0, 30.133333333333333, 30.233333333333334,...	[30.0, 30.133333333333333, 30.233333333333334,...	1	0	0	https://lichess.org/xf6vANYi	2021.01.03	04:10:57	Normal
39	2021	1	2	Black	1	33	anotheralias	Caro-Kann Defense	Main Line	B10	...	[480, 495, 508, 520, 527, 540, 552, 560, 543, ...	[8.0, 8.166666666666666, 8.333333333333334, 8....	[8.0, 8.25, 8.466666666666667, 8.6666666666666...	1	0	0	https://lichess.org/Oad70B2i	2021.01.02	11:05:36	Normal
40	2021	1	2	White	0	23	zypek	Owen Defense	Main Line	B00	...	[900, 904, 905, 898, 734, 731, 729, 722, 722, ...	[15.0, 15.0, 15.1, 15.083333333333334, 15.0666...	[15.0, 15.066666666666666, 15.083333333333334,...	0	1	0	https://lichess.org/q0tuVJFc	2021.01.02	10:40:35	Normal
41	2020	12	16	Black	1	45	Zaibee	Caro-Kann Defense	Advance Variation, Short Variation	B12	...	[900, 898, 895, 893, 889, 855, 849, 840, 838, ...	[15.0, 14.983333333333333, 14.883333333333333,...	[15.0, 14.966666666666667, 14.916666666666666,...	1	0	0	https://lichess.org/Lkvodw1h	2020.12.16	09:03:25	Normal
42	2020	12	16	White	1	35	Zaibee	French Defense	Mediterranean Defense	C00	...	[900, 898, 893, 888, 888, 882, 868, 866, 853, ...	[15.0, 14.95, 14.666666666666666, 14.566666666...	[15.0, 14.966666666666667, 14.883333333333333,...	1	0	0	https://lichess.org/8zMhRCOQ	2020.12.16	08:42:09	Normal
43	2020	12	15	Black	0	18	Keshawa_151059	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[300, 314, 328, 338, 350, 330, 332, 340, 309, ...	[5.0, 5.216666666666667, 5.433333333333334, 5....	[5.0, 5.233333333333333, 5.466666666666667, 5....	0	1	0	https://lichess.org/PJl6rQJY	2020.12.15	10:45:55	Normal
44	2020	12	14	White	1	41	Feuchtmacher69	Italian Game	Two Knights Defense, Polerio Defense	C58	...	[900, 890, 849, 833, 823, 812, 794, 787, 768, ...	[15.0, 14.933333333333334, 14.85, 14.466666666...	[15.0, 14.833333333333334, 14.15, 13.883333333...	1	0	0	https://lichess.org/iVWEZe4q	2020.12.14	06:45:01	Normal
45	2020	12	14	Black	0	10	OtakuYA99	Scotch Game	Classical Variation	C45	...	[1500, 1506, 1512, 1506, 1511, 1514, 1480, 140...	[25.0, 24.983333333333334, 24.933333333333334,...	[25.0, 25.1, 25.2, 25.1, 25.183333333333334, 2...	0	1	0	https://lichess.org/3GcDOMDI	2020.12.14	06:33:47	Normal
46	2020	12	14	Black	0	7	Nikoloz_rtskhiladze	Italian Game	Classical Variation, Greco Gambit, Traditional...	C54	...	[900, 898, 896, 891, 890, 884]	[15.0, 14.983333333333333, 14.933333333333334,...	[15.0, 14.966666666666667, 14.933333333333334,...	0	1	0	https://lichess.org/KI7iIHD4	2020.12.14	06:31:59	Normal
47	2020	12	14	Black	0	11	OtakuYA99	Caro-Kann Defense	Main Line	B10	...	[1500, 1495, 1501, 1501, 1497, 1500, 1505, 147...	[25.0, 24.833333333333332, 24.616666666666667,...	[25.0, 24.916666666666668, 25.016666666666666,...	0	1	0	https://lichess.org/4M7SXM3T	2020.12.14	06:17:19	Normal
48	2020	12	14	Black	0	61	Obrecht	Caro-Kann Defense	Main Line	B10	...	[900, 898, 890, 885, 877, 864, 860, 842, 826, ...	[15.0, 14.916666666666666, 14.75, 14.666666666...	[15.0, 14.966666666666667, 14.833333333333334,...	0	1	0	https://lichess.org/CBBhp0uF	2020.12.14	05:45:30	Time forfeit
49	2020	12	13	Black	1	39	Artem10let	Scotch Game	Schmidt Variation	C45	...	[10800, 10979, 11157, 11335, 11472, 11644, 118...	[180.0, 182.95, 185.88333333333333, 188.683333...	[180.0, 182.98333333333332, 185.95, 188.916666...	1	0	0	https://lichess.org/DT0tmBDD	2020.12.13	09:36:46	Time forfeit
50	2020	12	13	Black	0	23	no3h	Caro-Kann Defense	Exchange Variation	B13	...	[900, 907, 913, 910, 917, 922, 926, 932, 939, ...	[15.0, 15.083333333333334, 15.183333333333334,...	[15.0, 15.116666666666667, 15.216666666666667,...	0	1	0	https://lichess.org/IMS7HogD	2020.12.13	09:26:27	Normal

	Year	Month	Day	Color	Result	Moves	Opponent	Opening	Variation	ECO	...	BlackTotalSeconds	WhiteTotalMinutes	BlackTotalMinutes	Win	Loss	Draw	Site	UTCDate	UTCTime	Termination
0	2021	3	14	Black	1.0	16	carloslayun	Caro-Kann Defense	Main Line	B10	...	[600, 604, 610, 606, 604, 575, 549, 521, 495, ...	[10.0, 10.033333333333333, 9.45, 9.45, 9.51666...	[10.0, 10.066666666666666, 10.166666666666666,...	1	0	0	https://lichess.org/vMsQMTFK	2021.03.14	23:35:53	Normal
1	2021	3	8	White	1.0	23	piffolopuf	King's Pawn Game	Nimzowitsch Defense	B00	...	[1500, 1491, 1467, 1456, 1445, 1440, 1432, 142...	[25.0, 24.85, 24.783333333333335, 24.733333333...	[25.0, 24.85, 24.45, 24.266666666666666, 24.08...	1	0	0	https://lichess.org/YRZYOj4x	2021.03.08	06:40:18	Time forfeit
2	2021	3	6	White	1.0	40	grazia121	Vienna Game	Main Line	C25	...	[720, 718, 716, 712, 710, 708, 705, 702, 700, ...	[12.0, 11.933333333333334, 11.866666666666667,...	[12.0, 11.966666666666667, 11.933333333333334,...	1	0	0	https://lichess.org/KvaXPfTb	2021.03.06	21:59:24	Normal
3	2021	3	4	Black	1.0	18	Puckster2	Caro-Kann Defense	Main Line	B10	...	[900, 898, 897, 884, 876, 871, 851, 792, 759, ...	[15.0, 14.966666666666667, 14.9, 14.8166666666...	[15.0, 14.966666666666667, 14.95, 14.733333333...	1	0	0	https://lichess.org/AwokYQfB	2021.03.04	19:55:23	Normal
4	2021	3	3	White	1.0	25	katoks	French Defense	Advance Variation, Paulsen Attack	C02	...	[1200, 1195, 1192, 1189, 1187, 1183, 1181, 117...	[20.0, 19.85, 19.683333333333334, 19.616666666...	[20.0, 19.916666666666668, 19.866666666666667,...	1	0	0	https://lichess.org/KTYmmSVk	2021.03.03	06:39:13	Normal
5	2021	3	3	Black	0.0	32	avatar71	East Indian Defense	Main Line	A48	...	[1200, 1195, 1179, 1171, 1165, 1121, 1102, 106...	[20.0, 19.916666666666668, 19.866666666666667,...	[20.0, 19.916666666666668, 19.65, 19.516666666...	0	1	0	https://lichess.org/RCSXTepB	2021.03.03	06:11:35	Normal
6	2021	3	1	Black	0.0	20	asuvor	Caro-Kann Defense	Two Knights Attack	B10	...	[900, 912, 879, 879, 816, 807, 786, 799, 756, ...	[15.0, 15.2, 15.033333333333333, 14.9666666666...	[15.0, 15.2, 14.65, 14.65, 13.6, 13.45, 13.1, ...	0	1	0	https://lichess.org/CsFaZmYS	2021.03.01	08:55:53	Normal
7	2021	3	1	White	1.0	19	MiguelRojoImpar23	Vienna Game	Main Line	C25	...	[600, 606, 608, 611, 609, 613, 607, 609, 613, ...	[10.0, 10.1, 10.033333333333333, 9.8, 9.783333...	[10.0, 10.1, 10.133333333333333, 10.1833333333...	1	0	0	https://lichess.org/Atqqpc7H	2021.03.01	08:11:41	Normal
8	2021	2	26	Black	1.0	17	Firsin	Caro-Kann Defense	Classical Variation	B18	...	[900, 898, 893, 877, 839, 806, 780, 774, 718, ...	[15.0, 14.966666666666667, 14.866666666666667,...	[15.0, 14.966666666666667, 14.883333333333333,...	1	0	0	https://lichess.org/9XagE6Z8	2021.02.26	08:25:23	Time forfeit
9	2021	2	25	White	1.0	13	fazan_admin	Caro-Kann Defense	Maróczy Variation	B12	...	[840, 839, 836, 819, 818, 815, 812, 809, 805, ...	[14.0, 13.833333333333334, 13.733333333333333,...	[14.0, 13.983333333333333, 13.933333333333334,...	1	0	0	https://lichess.org/Zj8E3E31	2021.02.25	04:38:06	Normal
10	2021	2	25	Black	1.0	17	SergVish	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[1200, 1198, 1196, 1182, 1174, 1147, 1133, 112...	[20.0, 19.9, 19.85, 19.75, 19.633333333333333,...	[20.0, 19.966666666666665, 19.933333333333334,...	1	0	0	https://lichess.org/Sr0dqmy9	2021.02.25	04:24:35	Normal
11	2021	2	20	White	1.0	25	Zlatokosy	Sicilian Defense	Alapin Variation	B22	...	[1200, 1202, 1199, 1201, 1186, 1160, 1133, 113...	[20.0, 19.983333333333334, 20.0, 20.0666666666...	[20.0, 20.033333333333335, 19.983333333333334,...	1	0	0	https://lichess.org/rIhBEYZK	2021.02.20	22:46:33	Normal
12	2021	2	20	Black	1.0	42	JokerLiar	Caro-Kann Defense	Main Line	B10	...	[720, 724, 721, 693, 695, 675, 678, 677, 626, ...	[12.0, 12.083333333333334, 12.183333333333334,...	[12.0, 12.066666666666666, 12.016666666666667,...	1	0	0	https://lichess.org/YCQ5Xeqp	2021.02.20	22:14:22	Normal
13	2021	2	20	White	1.0	22	ecureuil_69	Vienna Game	Vienna Gambit	C26	...	[600, 598, 597, 592, 599, 605, 604, 607, 614, ...	[10.0, 10.083333333333334, 10.183333333333334,...	[10.0, 9.966666666666667, 9.95, 9.866666666666...	1	0	0	https://lichess.org/UEkS8vIW	2021.02.20	21:53:34	Normal
14	2021	2	19	Black	1.0	38	domenico61	Caro-Kann Defense	Hillbilly Attack	B10	...	[900, 913, 924, 932, 941, 912, 908, 914, 920, ...	[15.0, 15.216666666666667, 15.15, 15.366666666...	[15.0, 15.216666666666667, 15.4, 15.5333333333...	1	0	0	https://lichess.org/xuQIMbJK	2021.02.19	09:30:32	Normal
15	2021	2	19	Black	0.0	38	PawnBlusher	Caro-Kann Defense	Main Line	B10	...	[600, 609, 617, 622, 597, 604, 597, 587, 582, ...	[10.0, 10.0, 9.783333333333333, 9.85, 9.966666...	[10.0, 10.15, 10.283333333333333, 10.366666666...	0	1	0	https://lichess.org/aNEPnO2h	2021.02.19	08:59:24	Normal
16	2021	2	18	Black	1.0	14	AlejandroM	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[900, 898, 881, 874, 868, 861, 857, 842, 824, ...	[15.0, 14.933333333333334, 14.733333333333333,...	[15.0, 14.966666666666667, 14.683333333333334,...	1	0	0	https://lichess.org/3E3H9SYJ	2021.02.18	23:49:53	Normal
17	2021	2	16	White	1.0	19	mladendjuric55	Vienna Game	Vienna Gambit	C26	...	[900, 897, 889, 886, 868, 860, 845, 815, 789, ...	[15.0, 14.95, 14.883333333333333, 14.75, 14.6,...	[15.0, 14.95, 14.816666666666666, 14.766666666...	1	0	0	https://lichess.org/1uCoQ5xN	2021.02.16	07:50:15	Normal
18	2021	1	25	Black	1.0	43	arcnaka	Indian Game	Main Line	A45	...	[900, 895, 885, 870, 865, 848, 807, 775, 748, ...	[15.0, 14.6, 14.566666666666666, 14.5333333333...	[15.0, 14.916666666666666, 14.75, 14.5, 14.416...	1	0	0	https://lichess.org/41y1I6dI	2021.01.25	04:15:58	Normal
19	2021	1	24	White	0.5	34	jackbauerpwrhour	Vienna Game	Anderssen Defense	C25	...	[900, 901, 909, 918, 920, 933, 937, 936, 862, ...	[15.0, 15.116666666666667, 15.216666666666667,...	[15.0, 15.016666666666667, 15.15, 15.3, 15.333...	0	0	1	https://lichess.org/TBOfLX2P	2021.01.24	03:25:22	Normal
20	2021	1	17	White	1.0	29	Ceron68	French Defense	Classical Variation, Steinitz Variation	C11	...	[900, 903, 905, 909, 911, 911, 894, 883, 871, ...	[15.0, 15.016666666666667, 14.983333333333333,...	[15.0, 15.05, 15.083333333333334, 15.15, 15.18...	1	0	0	https://lichess.org/FHDuWQXK	2021.01.17	22:53:38	Normal
21	2021	1	17	White	0.0	47	Master_Don_Pundon	Scandinavian Defense	Mieses-Kotroc Variation	B01	...	[480, 480, 478, 475, 473, 471, 468, 455, 450, ...	[8.0, 7.783333333333333, 7.75, 7.6, 7.53333333...	[8.0, 8.0, 7.966666666666667, 7.91666666666666...	0	1	0	https://lichess.org/BdrJ9pMU	2021.01.17	22:36:11	Time forfeit
22	2021	1	16	White	1.0	25	buongiorgio1	Vienna Game	Anderssen Defense	C25	...	[2700, 2702, 2701, 2700, 2693, 2674, 2661, 258...	[45.0, 45.13333333333333, 45.13333333333333, 4...	[45.0, 45.03333333333333, 45.016666666666666, ...	1	0	0	https://lichess.org/lGmFihVm	2021.01.16	21:52:18	Normal
23	2021	1	15	Black	0.5	46	Yousefaboelmagd	Caro-Kann Defense	Maróczy Variation	B12	...	[600, 609, 597, 586, 569, 571, 509, 507, 486, ...	[10.0, 10.116666666666667, 10.25, 10.383333333...	[10.0, 10.15, 9.95, 9.766666666666667, 9.48333...	0	0	1	https://lichess.org/JmzNA3Cm	2021.01.15	21:01:43	Normal
24	2021	1	14	Black	1.0	37	RichardSchach	Caro-Kann Defense	Main Line	B10	...	[300, 306, 305, 309, 316, 317, 321, 328, 334, ...	[5.0, 5.116666666666666, 5.2, 5.26666666666666...	[5.0, 5.1, 5.083333333333333, 5.15, 5.26666666...	1	0	0	https://lichess.org/aeV5aB0T	2021.01.14	23:23:12	Normal
25	2021	1	14	White	0.0	4	Hamdyamin28	Vienna Game	Main Line	C25	...	[1200, 1187, 1183, 1169]	[20.0, 20.066666666666666, 19.583333333333332,...	[20.0, 19.783333333333335, 19.716666666666665,...	0	1	0	https://lichess.org/JZS5MfJY	2021.01.14	23:21:18	Normal
26	2021	1	14	Black	0.0	12	psxt12	Caro-Kann Defense	Main Line	B10	...	[1200, 1198, 1197, 1185, 1182, 1180, 1167, 108...	[20.0, 19.816666666666666, 19.783333333333335,...	[20.0, 19.966666666666665, 19.95, 19.75, 19.7,...	0	1	0	https://lichess.org/LhTPZBxH	2021.01.14	23:15:59	Normal
27	2021	1	14	White	0.0	12	whichful	Bishop's Opening	Vienna Hybrid	C24	...	[960, 964, 963, 957, 963, 964, 971, 971, 976, ...	[16.0, 16.116666666666667, 16.166666666666668,...	[16.0, 16.066666666666666, 16.05, 15.95, 16.05...	0	1	0	https://lichess.org/5n52ZaQb	2021.01.14	23:10:49	Normal
28	2021	1	14	Black	0.0	14	chemaqe	London System	Main Line	A48	...	[900, 899, 892, 889, 884, 826, 807, 786, 781, ...	[15.0, 14.933333333333334, 14.816666666666666,...	[15.0, 14.983333333333333, 14.866666666666667,...	0	1	0	https://lichess.org/YDjgdKaK	2021.01.14	23:02:57	Normal
29	2021	1	14	Black	1.0	21	Prof-jones	King's Indian Defense	Four Pawns Attack	E76	...	[1800, 1816, 1834, 1853, 1870, 1888, 1906, 191...	[30.0, 30.3, 30.6, 30.9, 31.166666666666668, 3...	[30.0, 30.266666666666666, 30.566666666666666,...	1	0	0	https://lichess.org/Ze8jkIj9	2021.01.14	01:42:52	Normal
30	2021	1	13	White	0.5	40	aconito	Sicilian Defense	Alapin Variation	B22	...	[780, 777, 769, 738, 729, 717, 712, 707, 703, ...	[13.0, 12.966666666666667, 12.9, 12.5, 11.8833...	[13.0, 12.95, 12.816666666666666, 12.3, 12.15,...	0	0	1	https://lichess.org/JwOD3Uvu	2021.01.13	22:04:56	Normal
31	2021	1	12	White	1.0	30	ITAKAR	Vienna Game	Vienna Gambit	C26	...	[900, 888, 883, 882, 848, 830, 824, 817, 809, ...	[15.0, 14.95, 14.933333333333334, 14.85, 14.8,...	[15.0, 14.8, 14.716666666666667, 14.7, 14.1333...	1	0	0	https://lichess.org/xgw8uOYJ	2021.01.12	21:51:15	Normal
32	2021	1	9	Black	1.0	27	Hamzzza-Nailkin	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[540, 553, 566, 579, 592, 604, 599, 607, 620, ...	[9.0, 9.183333333333334, 9.316666666666666, 9....	[9.0, 9.216666666666667, 9.433333333333334, 9....	1	0	0	https://lichess.org/7keBysFD	2021.01.09	23:46:46	Normal
33	2021	1	9	White	1.0	29	Hikal23	Rat Defense	Small Center Defense	C00	...	[2700, 2707, 2713, 2706, 2698, 2695, 2702, 269...	[45.0, 45.03333333333333, 44.86666666666667, 4...	[45.0, 45.11666666666667, 45.21666666666667, 4...	1	0	0	https://lichess.org/vhplZygT	2021.01.09	03:27:41	Normal
34	2021	1	8	Black	1.0	48	JORGEAPRA	Caro-Kann Defense	Exchange Variation	B13	...	[900, 912, 926, 936, 936, 939, 944, 929, 934, ...	[15.0, 15.15, 15.216666666666667, 15.333333333...	[15.0, 15.2, 15.433333333333334, 15.6, 15.6, 1...	1	0	0	https://lichess.org/bHqP3Sq2	2021.01.08	22:47:13	Normal
35	2021	1	4	White	0.5	17	anthony_abogado21PWD	Vienna Game	Vienna Gambit, Paulsen Attack	C29	...	[1800, 1706, 1672, 1616, 1564, 1519, 1475, 141...	[30.0, 30.1, 30.216666666666665, 30.2333333333...	[30.0, 28.433333333333334, 27.866666666666667,...	0	0	1	https://lichess.org/NEhQ6lVr	2021.01.04	02:07:56	Normal
36	2021	1	4	Black	0.0	56	Runa1791Runoid	Caro-Kann Defense	Exchange Variation	B13	...	[1800, 1811, 1820, 1822, 1832, 1817, 1826, 182...	[30.0, 29.866666666666667, 29.233333333333334,...	[30.0, 30.183333333333334, 30.333333333333332,...	0	1	0	https://lichess.org/oDSswIx0	2021.01.04	01:02:57	Normal
37	2021	1	3	Black	1.0	24	Numisma9999	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[1080, 1089, 1085, 1093, 1101, 1109, 1114, 111...	[18.0, 17.983333333333334, 17.983333333333334,...	[18.0, 18.15, 18.083333333333332, 18.216666666...	1	0	0	https://lichess.org/PDHTPoBA	2021.01.03	07:26:17	Normal
38	2021	1	3	White	1.0	40	InBetweenWins	Sicilian Defense	Alapin Variation	B22	...	[1800, 1808, 1814, 1823, 1811, 1812, 1800, 179...	[30.0, 30.133333333333333, 30.233333333333334,...	[30.0, 30.133333333333333, 30.233333333333334,...	1	0	0	https://lichess.org/xf6vANYi	2021.01.03	04:10:57	Normal
39	2021	1	2	Black	1.0	33	anotheralias	Caro-Kann Defense	Main Line	B10	...	[480, 495, 508, 520, 527, 540, 552, 560, 543, ...	[8.0, 8.166666666666666, 8.333333333333334, 8....	[8.0, 8.25, 8.466666666666667, 8.6666666666666...	1	0	0	https://lichess.org/Oad70B2i	2021.01.02	11:05:36	Normal
40	2021	1	2	White	0.0	23	zypek	Owen Defense	Main Line	B00	...	[900, 904, 905, 898, 734, 731, 729, 722, 722, ...	[15.0, 15.0, 15.1, 15.083333333333334, 15.0666...	[15.0, 15.066666666666666, 15.083333333333334,...	0	1	0	https://lichess.org/q0tuVJFc	2021.01.02	10:40:35	Normal
41	2020	12	16	Black	1.0	45	Zaibee	Caro-Kann Defense	Advance Variation, Short Variation	B12	...	[900, 898, 895, 893, 889, 855, 849, 840, 838, ...	[15.0, 14.983333333333333, 14.883333333333333,...	[15.0, 14.966666666666667, 14.916666666666666,...	1	0	0	https://lichess.org/Lkvodw1h	2020.12.16	09:03:25	Normal
42	2020	12	16	White	1.0	35	Zaibee	French Defense	Mediterranean Defense	C00	...	[900, 898, 893, 888, 888, 882, 868, 866, 853, ...	[15.0, 14.95, 14.666666666666666, 14.566666666...	[15.0, 14.966666666666667, 14.883333333333333,...	1	0	0	https://lichess.org/8zMhRCOQ	2020.12.16	08:42:09	Normal
43	2020	12	15	Black	0.0	18	Keshawa_151059	Caro-Kann Defense	Advance Variation, Botvinnik-Carls Defense	B12	...	[300, 314, 328, 338, 350, 330, 332, 340, 309, ...	[5.0, 5.216666666666667, 5.433333333333334, 5....	[5.0, 5.233333333333333, 5.466666666666667, 5....	0	1	0	https://lichess.org/PJl6rQJY	2020.12.15	10:45:55	Normal
44	2020	12	14	White	1.0	41	Feuchtmacher69	Italian Game	Two Knights Defense, Polerio Defense	C58	...	[900, 890, 849, 833, 823, 812, 794, 787, 768, ...	[15.0, 14.933333333333334, 14.85, 14.466666666...	[15.0, 14.833333333333334, 14.15, 13.883333333...	1	0	0	https://lichess.org/iVWEZe4q	2020.12.14	06:45:01	Normal
45	2020	12	14	Black	0.0	10	OtakuYA99	Scotch Game	Classical Variation	C45	...	[1500, 1506, 1512, 1506, 1511, 1514, 1480, 140...	[25.0, 24.983333333333334, 24.933333333333334,...	[25.0, 25.1, 25.2, 25.1, 25.183333333333334, 2...	0	1	0	https://lichess.org/3GcDOMDI	2020.12.14	06:33:47	Normal
46	2020	12	14	Black	0.0	7	Nikoloz_rtskhiladze	Italian Game	Classical Variation, Greco Gambit, Traditional...	C54	...	[900, 898, 896, 891, 890, 884]	[15.0, 14.983333333333333, 14.933333333333334,...	[15.0, 14.966666666666667, 14.933333333333334,...	0	1	0	https://lichess.org/KI7iIHD4	2020.12.14	06:31:59	Normal
47	2020	12	14	Black	0.0	11	OtakuYA99	Caro-Kann Defense	Main Line	B10	...	[1500, 1495, 1501, 1501, 1497, 1500, 1505, 147...	[25.0, 24.833333333333332, 24.616666666666667,...	[25.0, 24.916666666666668, 25.016666666666666,...	0	1	0	https://lichess.org/4M7SXM3T	2020.12.14	06:17:19	Normal
48	2020	12	14	Black	0.0	61	Obrecht	Caro-Kann Defense	Main Line	B10	...	[900, 898, 890, 885, 877, 864, 860, 842, 826, ...	[15.0, 14.916666666666666, 14.75, 14.666666666...	[15.0, 14.966666666666667, 14.833333333333334,...	0	1	0	https://lichess.org/CBBhp0uF	2020.12.14	05:45:30	Time forfeit
49	2020	12	13	Black	1.0	39	Artem10let	Scotch Game	Schmidt Variation	C45	...	[10800, 10979, 11157, 11335, 11472, 11644, 118...	[180.0, 182.95, 185.88333333333333, 188.683333...	[180.0, 182.98333333333332, 185.95, 188.916666...	1	0	0	https://lichess.org/DT0tmBDD	2020.12.13	09:36:46	Time forfeit
50	2020	12	13	Black	0.0	23	no3h	Caro-Kann Defense	Exchange Variation	B13	...	[900, 907, 913, 910, 917, 922, 926, 932, 939, ...	[15.0, 15.083333333333334, 15.183333333333334,...	[15.0, 15.116666666666667, 15.216666666666667,...	0	1	0	https://lichess.org/IMS7HogD	2020.12.13	09:26:27	Normal

	GP	Moves	Win	Loss	Draw	PPG	REL	Total Rating Gain	Rating Difference	RatingChange	Most common variation	Most played in
Opening
Vienna Game	9	23.3	6	1	2	0.778	0.028	112	45	12.44	Main Line	Rapid
French Defense	3	29.7	3	0	0	1.000	0.250	195	-85	65.00	Advance Variation, Paulsen Attack	Rapid
Sicilian Defense	3	35.0	2	0	1	0.833	0.083	130	52	43.33	Alapin Variation	Rapid
Bishop's Opening	1	12.0	0	1	0	0.000	-0.750	-31	5	-31.00	Vienna Hybrid	Rapid
Caro-Kann Defense	1	13.0	1	0	0	1.000	0.250	11	144	11.00	Maróczy Variation	Rapid
Italian Game	1	41.0	1	0	0	1.000	0.250	204	-482	204.00	Two Knights Defense, Polerio Defense	Rapid
King's Pawn Game	1	23.0	1	0	0	1.000	0.250	26	78	26.00	Nimzowitsch Defense	Classical
Owen Defense	1	23.0	0	1	0	0.000	-0.750	-68	24	-68.00	Main Line	Rapid
Rat Defense	1	29.0	1	0	0	1.000	0.250	43	47	43.00	Small Center Defense	Classical
Scandinavian Defense	1	47.0	0	1	0	0.000	-0.750	-20	-27	-20.00	Mieses-Kotroc Variation	Rapid

	GP	Moves	Win	Loss	Draw	PPG	REL	Total Rating Gain	Rating Difference	RatingChange	Most played in
Variation
Main Line	9	29.8	5	4	0	0.556	-0.058	-269	23	-29.89	Rapid
Advance Variation, Botvinnik-Carls Defense	5	20.0	4	1	0	0.800	0.186	90	-36	18.00	Rapid
Exchange Variation	3	42.3	1	2	0	0.333	-0.281	-215	-51	-71.67	Classical
Advance Variation, Short Variation	1	45.0	1	0	0	1.000	0.386	90	-41	90.00	Rapid
Classical Variation	1	17.0	1	0	0	1.000	0.386	18	0	18.00	Rapid
Hillbilly Attack	1	38.0	1	0	0	1.000	0.386	27	100	27.00	Classical
Maróczy Variation	1	46.0	0	0	1	0.500	-0.114	3	-50	3.00	Rapid
Two Knights Attack	1	20.0	0	1	0	0.000	-0.614	-36	20	-36.00	Classical

	GP	Moves	Win	Loss	Draw	PPG	REL	Total Rating Gain	Rating Difference	RatingChange	Most common variation	Most played in
Opening
Caro-Kann Defense	22	30.0	13	8	1	0.614	0.045	-292	-5	-13.27	Main Line	Rapid
Scotch Game	2	24.5	1	1	0	0.500	-0.069	279	-96	139.50	Classical Variation	Classical
East Indian Defense	1	32.0	0	1	0	0.000	-0.569	-19	87	-19.00	Main Line	Rapid
Indian Game	1	43.0	1	0	0	1.000	0.431	21	10	21.00	Main Line	Rapid
Italian Game	1	7.0	0	1	0	0.000	-0.569	-28	-413	-28.00	Classical Variation, Greco Gambit, Traditional...	Rapid
King's Indian Defense	1	21.0	1	0	0	1.000	0.431	38	50	38.00	Four Pawns Attack	Classical
London System	1	14.0	0	1	0	0.000	-0.569	-44	117	-44.00	Main Line	Rapid

	Count	Win	Loss	Draw	Result	Rating	OpponentRating	Moves	Current Rating	EVAL	Rating After Move
WhiteMoves
Nc3	10	6	2	2	0.7	1688	1646.5	22.2	0.33	-0.16	0
Nf3	1	1	0	0	1.0	1212	1694.0	41.0	0.33	-0.09	0

Chess : Data Analysis & Visualization¶

Data provided by Lichess API tool¶

Table of Contents¶

1. Setup¶

2. CSV loading¶

3. Basic User Stats¶

4. Openings¶

5. Personal Opening Tree¶

6. Engine Evaluation¶

7. Time Analysis¶

8. Seasons¶

9. Win Predictor¶

1. Setup¶

Read here first!¶

Input: PGN file¶

Output: Sorted CSV to create a data frame with the following columns:¶

Or, an alternate view:¶

Once you have done that, you will have a PGN file downloaded on your computer. Move that to the same folder as this ipynb file, and type in the name of the file without .PGN in the cell below.¶

Also input your Lichess username. Capitalization matters.¶

To build the CSV from the given format, many adjustments were needed:¶

1. parse_pgn(): Transform the first 17/18 row entries into columns with regex¶

2. parse_notation(): 18th row, game notation¶

3. time_fn(): Handling move time increment¶

4. format_df(): Formatting columns¶

Run the cells below to generate a CSV. Drag the CSV into the folder with this ipynb file (if it isn't already), and proceed to later steps.¶

Run Setup.¶

(Optional): view the table (and check for potential errors).¶

Download csv file (go to File->Open or the folder to see it, or through local files).¶

2. CSV loading¶

3. Basic User Stats¶

3.1 Win/Loss

3.2 Rating Difference vs Rating Change

3.3.1 User Rating By Date

3.3.2 User Rating Over Time

3.4 Performance against opponent rating

4. Openings¶

4.1 Openings played as White/Black

4.2 Opening Variations played as White/Black

5. Personal Opening Tree¶

Personal Opening Tree¶

6. Engine Evaluation¶

6.0 Setup

6.1 Engine Evaluation against Opponent

6.2 Opening vs Engine Evaluation

6.3 Opening Variation vs Engine Evaluation

There are two functions below:¶

Important: the variation is included in the PGN file as the part after the ":". If no ":" appears for a game entry, then the variation is simply considered "Main Line".¶

6.4 Engine Evaluation on Piece Moved

6.5 Specified Eval + Move Number Statistics

7. Time Analysis¶

7.0 Setup

7.1 Move # vs Time Spent

7.2 Move # vs Time Remaining

7.3 Engine Evaluation vs Time Spent

7.4 Move Rating vs Time Spent

7.5 Piece Moved vs Time Spent

8. Seasons¶

8.1 Seasons

8.2 Season Graphs

9. Win Predictor using PCA + Multinomial Logistic Regression¶

9.0 PCA + Model

PCA¶

Model¶

9.1 Model 1 : Predictor using model_df

9.2 Model 2 : Predictor without game knowledge

9.3 Model 3 : Predictor with opening knowledge

1. Setup ¶

2. CSV loading ¶

3. Basic User Stats ¶

4. Openings ¶

5. Personal Opening Tree ¶

6. Engine Evaluation ¶

7. Time Analysis ¶

8. Seasons ¶

9. Win Predictor ¶